{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2d2YLOtGEfRB"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['PATH'] = f\"{os.environ['PATH']}:/root/.local/bin\"\n",
        "os.environ['PATH'] = f\"{os.environ['PATH']}:/opt/conda/lib/python3.6/site-packages\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "W92wxK2aevM6"
      },
      "outputs": [],
      "source": [
        "# Imports here\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import numpy as np\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "from torchvision import datasets, models, transforms\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data\n",
        "import pandas as pd\n",
        "#import helper\n",
        "from collections import OrderedDict\n",
        "from PIL import Image\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "J3yHf5FJ4oFW"
      },
      "outputs": [],
      "source": [
        "data_dir = '/content/udacity-image-classification/flowers'\n",
        "train_dir = data_dir + '/train'\n",
        "valid_dir = data_dir + '/valid'\n",
        "test_dir = data_dir + '/test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "DJ-p_CoC4vJn"
      },
      "outputs": [],
      "source": [
        "# TODO: Define your transforms for the training, validation, and testing sets\n",
        "train_data_transforms = transforms.Compose ([transforms.RandomRotation (30),\n",
        "                                             transforms.RandomResizedCrop (224),\n",
        "                                             transforms.RandomHorizontalFlip (),\n",
        "                                             transforms.ToTensor (),\n",
        "                                             transforms.Normalize ([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])\n",
        "                                            ])\n",
        "\n",
        "valid_data_transforms = transforms.Compose ([transforms.Resize (255),\n",
        "                                             transforms.CenterCrop (224),\n",
        "                                             transforms.ToTensor (),\n",
        "                                             transforms.Normalize ([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])\n",
        "                                            ])\n",
        "\n",
        "test_data_transforms = transforms.Compose ([transforms.Resize (255),\n",
        "                                             transforms.CenterCrop (224),\n",
        "                                             transforms.ToTensor (),\n",
        "                                             transforms.Normalize ([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])\n",
        "                                            ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "iLt-NZfQ4zgQ"
      },
      "outputs": [],
      "source": [
        "# TODO: Load the datasets with ImageFolder\n",
        "train_image_datasets = datasets.ImageFolder (train_dir, transform = train_data_transforms)\n",
        "valid_image_datasets = datasets.ImageFolder (valid_dir, transform = valid_data_transforms)\n",
        "test_image_datasets = datasets.ImageFolder (test_dir, transform = test_data_transforms)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "XfKUrfVa5BxN"
      },
      "outputs": [],
      "source": [
        "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
        "train_loader = torch.utils.data.DataLoader(train_image_datasets, batch_size = 64, shuffle = True)\n",
        "valid_loader = torch.utils.data.DataLoader(valid_image_datasets, batch_size = 64, shuffle = True)\n",
        "test_loader = torch.utils.data.DataLoader(test_image_datasets, batch_size = 64, shuffle = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWCQq9Xl5FfF",
        "outputId": "77cd20dc-d9f3-4687-faa9-078eb3a4daa9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([64, 3, 224, 224])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inputs, labels = next(iter(train_loader))\n",
        "inputs [0,:]\n",
        "inputs.size ()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwxZKcdJ5Jj-",
        "outputId": "ff75193d-2834-4f8e-b11b-c5e53d2f84a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'1': 0,\n",
              " '10': 1,\n",
              " '100': 2,\n",
              " '101': 3,\n",
              " '102': 4,\n",
              " '11': 5,\n",
              " '12': 6,\n",
              " '13': 7,\n",
              " '14': 8,\n",
              " '15': 9,\n",
              " '16': 10,\n",
              " '17': 11,\n",
              " '18': 12,\n",
              " '19': 13,\n",
              " '2': 14,\n",
              " '20': 15,\n",
              " '21': 16,\n",
              " '22': 17,\n",
              " '23': 18,\n",
              " '24': 19,\n",
              " '25': 20,\n",
              " '26': 21,\n",
              " '27': 22,\n",
              " '28': 23,\n",
              " '29': 24,\n",
              " '3': 25,\n",
              " '30': 26,\n",
              " '31': 27,\n",
              " '32': 28,\n",
              " '33': 29,\n",
              " '34': 30,\n",
              " '35': 31,\n",
              " '36': 32,\n",
              " '37': 33,\n",
              " '38': 34,\n",
              " '39': 35,\n",
              " '4': 36,\n",
              " '40': 37,\n",
              " '41': 38,\n",
              " '42': 39,\n",
              " '43': 40,\n",
              " '44': 41,\n",
              " '45': 42,\n",
              " '46': 43,\n",
              " '47': 44,\n",
              " '48': 45,\n",
              " '49': 46,\n",
              " '5': 47,\n",
              " '50': 48,\n",
              " '51': 49,\n",
              " '52': 50,\n",
              " '53': 51,\n",
              " '54': 52,\n",
              " '55': 53,\n",
              " '56': 54,\n",
              " '57': 55,\n",
              " '58': 56,\n",
              " '59': 57,\n",
              " '6': 58,\n",
              " '60': 59,\n",
              " '61': 60,\n",
              " '62': 61,\n",
              " '63': 62,\n",
              " '64': 63,\n",
              " '65': 64,\n",
              " '66': 65,\n",
              " '67': 66,\n",
              " '68': 67,\n",
              " '69': 68,\n",
              " '7': 69,\n",
              " '70': 70,\n",
              " '71': 71,\n",
              " '72': 72,\n",
              " '73': 73,\n",
              " '74': 74,\n",
              " '75': 75,\n",
              " '76': 76,\n",
              " '77': 77,\n",
              " '78': 78,\n",
              " '79': 79,\n",
              " '8': 80,\n",
              " '80': 81,\n",
              " '81': 82,\n",
              " '82': 83,\n",
              " '83': 84,\n",
              " '84': 85,\n",
              " '85': 86,\n",
              " '86': 87,\n",
              " '87': 88,\n",
              " '88': 89,\n",
              " '89': 90,\n",
              " '9': 91,\n",
              " '90': 92,\n",
              " '91': 93,\n",
              " '92': 94,\n",
              " '93': 95,\n",
              " '94': 96,\n",
              " '95': 97,\n",
              " '96': 98,\n",
              " '97': 99,\n",
              " '98': 100,\n",
              " '99': 101}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_image_datasets.class_to_idx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_JhJaoL5NYQ",
        "outputId": "c328a27d-4a16-4061-9823-2234b24e77d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'21': 'fire lily',\n",
              " '3': 'canterbury bells',\n",
              " '45': 'bolero deep blue',\n",
              " '1': 'pink primrose',\n",
              " '34': 'mexican aster',\n",
              " '27': 'prince of wales feathers',\n",
              " '7': 'moon orchid',\n",
              " '16': 'globe-flower',\n",
              " '25': 'grape hyacinth',\n",
              " '26': 'corn poppy',\n",
              " '79': 'toad lily',\n",
              " '39': 'siam tulip',\n",
              " '24': 'red ginger',\n",
              " '67': 'spring crocus',\n",
              " '35': 'alpine sea holly',\n",
              " '32': 'garden phlox',\n",
              " '10': 'globe thistle',\n",
              " '6': 'tiger lily',\n",
              " '93': 'ball moss',\n",
              " '33': 'love in the mist',\n",
              " '9': 'monkshood',\n",
              " '102': 'blackberry lily',\n",
              " '14': 'spear thistle',\n",
              " '19': 'balloon flower',\n",
              " '100': 'blanket flower',\n",
              " '13': 'king protea',\n",
              " '49': 'oxeye daisy',\n",
              " '15': 'yellow iris',\n",
              " '61': 'cautleya spicata',\n",
              " '31': 'carnation',\n",
              " '64': 'silverbush',\n",
              " '68': 'bearded iris',\n",
              " '63': 'black-eyed susan',\n",
              " '69': 'windflower',\n",
              " '62': 'japanese anemone',\n",
              " '20': 'giant white arum lily',\n",
              " '38': 'great masterwort',\n",
              " '4': 'sweet pea',\n",
              " '86': 'tree mallow',\n",
              " '101': 'trumpet creeper',\n",
              " '42': 'daffodil',\n",
              " '22': 'pincushion flower',\n",
              " '2': 'hard-leaved pocket orchid',\n",
              " '54': 'sunflower',\n",
              " '66': 'osteospermum',\n",
              " '70': 'tree poppy',\n",
              " '85': 'desert-rose',\n",
              " '99': 'bromelia',\n",
              " '87': 'magnolia',\n",
              " '5': 'english marigold',\n",
              " '92': 'bee balm',\n",
              " '28': 'stemless gentian',\n",
              " '97': 'mallow',\n",
              " '57': 'gaura',\n",
              " '40': 'lenten rose',\n",
              " '47': 'marigold',\n",
              " '59': 'orange dahlia',\n",
              " '48': 'buttercup',\n",
              " '55': 'pelargonium',\n",
              " '36': 'ruby-lipped cattleya',\n",
              " '91': 'hippeastrum',\n",
              " '29': 'artichoke',\n",
              " '71': 'gazania',\n",
              " '90': 'canna lily',\n",
              " '18': 'peruvian lily',\n",
              " '98': 'mexican petunia',\n",
              " '8': 'bird of paradise',\n",
              " '30': 'sweet william',\n",
              " '17': 'purple coneflower',\n",
              " '52': 'wild pansy',\n",
              " '84': 'columbine',\n",
              " '12': \"colt's foot\",\n",
              " '11': 'snapdragon',\n",
              " '96': 'camellia',\n",
              " '23': 'fritillary',\n",
              " '50': 'common dandelion',\n",
              " '44': 'poinsettia',\n",
              " '53': 'primula',\n",
              " '72': 'azalea',\n",
              " '65': 'californian poppy',\n",
              " '80': 'anthurium',\n",
              " '76': 'morning glory',\n",
              " '37': 'cape flower',\n",
              " '56': 'bishop of llandaff',\n",
              " '60': 'pink-yellow dahlia',\n",
              " '82': 'clematis',\n",
              " '58': 'geranium',\n",
              " '75': 'thorn apple',\n",
              " '41': 'barbeton daisy',\n",
              " '95': 'bougainvillea',\n",
              " '43': 'sword lily',\n",
              " '83': 'hibiscus',\n",
              " '78': 'lotus lotus',\n",
              " '88': 'cyclamen',\n",
              " '94': 'foxglove',\n",
              " '81': 'frangipani',\n",
              " '74': 'rose',\n",
              " '89': 'watercress',\n",
              " '73': 'water lily',\n",
              " '46': 'wallflower',\n",
              " '77': 'passion flower',\n",
              " '51': 'petunia'}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import json\n",
        "\n",
        "with open(r'/content/udacity-image-classification/cat_to_name.json') as f:\n",
        "    cat_to_name = json.load(f)\n",
        "\n",
        "len (cat_to_name)\n",
        "cat_to_name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvJr93Do5WJB",
        "outputId": "d5c31d6e-b6f3-42f3-9a1d-73436e3b4474"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ],
      "source": [
        "model = models.alexnet (pretrained = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x4EIWRoE5cls",
        "outputId": "1858076e-e621-47d5-c022-f5c597bf15a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AlexNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
              "  (classifier): Sequential(\n",
              "    (fc1): Linear(in_features=9216, out_features=4096, bias=True)\n",
              "    (relu1): ReLU()\n",
              "    (dropout1): Dropout(p=0.3, inplace=False)\n",
              "    (fc2): Linear(in_features=4096, out_features=2048, bias=True)\n",
              "    (relu2): ReLU()\n",
              "    (dropout2): Dropout(p=0.3, inplace=False)\n",
              "    (fc3): Linear(in_features=2048, out_features=102, bias=True)\n",
              "    (output): LogSoftmax(dim=1)\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# updating classifer in the network\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "classifier = nn.Sequential  (OrderedDict ([\n",
        "                            ('fc1', nn.Linear (9216, 4096)),\n",
        "                            ('relu1', nn.ReLU ()),\n",
        "                            ('dropout1', nn.Dropout (p = 0.3)),\n",
        "                            ('fc2', nn.Linear (4096, 2048)),\n",
        "                            ('relu2', nn.ReLU ()),\n",
        "                            ('dropout2', nn.Dropout (p = 0.3)),\n",
        "                            ('fc3', nn.Linear (2048, 102)),\n",
        "                            ('output', nn.LogSoftmax (dim =1))\n",
        "                            ]))\n",
        "model.classifier = classifier\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "4gn0Xu8v5hmg"
      },
      "outputs": [],
      "source": [
        "#initializing criterion and optimizer\n",
        "criterion = nn.NLLLoss ()\n",
        "optimizer = optim.Adam (model.classifier.parameters (), lr = 0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ALuRVu0S5lIG"
      },
      "outputs": [],
      "source": [
        "# Defining validation\n",
        "def validation(model, valid_loader, criterion):\n",
        "    model.to ('cpu')\n",
        "\n",
        "    valid_loss = 0\n",
        "    accuracy = 0\n",
        "    for inputs, labels in valid_loader:\n",
        "\n",
        "        inputs, labels = inputs.to('cpu'), labels.to('cpu')\n",
        "        output = model.forward(inputs)\n",
        "        valid_loss += criterion(output, labels).item()\n",
        "\n",
        "        ps = torch.exp(output)\n",
        "        equality = (labels.data == ps.max(dim=1)[1])\n",
        "        accuracy += equality.type(torch.FloatTensor).mean()\n",
        "\n",
        "    return valid_loss, accuracy\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1c-eO7Z57yrQ",
        "outputId": "0e12f3c4-0800-47fb-b6ed-3851f97f8c26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n"
          ]
        }
      ],
      "source": [
        "!nvcc --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDcqYaza8EYg",
        "outputId": "92ca6f52-7453-4ba0-ef15-26d2f3acaced"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "System has not been booted with systemd as init system (PID 1). Can't operate.\n",
            "Failed to connect to bus: Host is down\n",
            "Failed to talk to init daemon.\n"
          ]
        }
      ],
      "source": [
        "!shutdown -r now"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gE9engDG5pNK",
        "outputId": "902b78ca-fa99-4453-8732-634db8797c70"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1/7..  Training Loss: 1.593..  Valid Loss: 1.217..  Valid Accuracy: 66.889%\n",
            "Epoch: 1/7..  Training Loss: 1.847..  Valid Loss: 1.052..  Valid Accuracy: 72.139%\n",
            "Epoch: 2/7..  Training Loss: 0.661..  Valid Loss: 0.928..  Valid Accuracy: 74.889%\n",
            "Epoch: 2/7..  Training Loss: 1.651..  Valid Loss: 0.903..  Valid Accuracy: 75.611%\n",
            "Epoch: 2/7..  Training Loss: 1.551..  Valid Loss: 0.844..  Valid Accuracy: 76.293%\n",
            "Epoch: 3/7..  Training Loss: 1.254..  Valid Loss: 0.849..  Valid Accuracy: 75.692%\n",
            "Epoch: 3/7..  Training Loss: 1.433..  Valid Loss: 0.779..  Valid Accuracy: 77.087%\n",
            "Epoch: 4/7..  Training Loss: 0.407..  Valid Loss: 0.734..  Valid Accuracy: 78.889%\n",
            "Epoch: 4/7..  Training Loss: 1.309..  Valid Loss: 0.754..  Valid Accuracy: 78.803%\n",
            "Epoch: 4/7..  Training Loss: 1.425..  Valid Loss: 0.767..  Valid Accuracy: 77.067%\n",
            "Epoch: 5/7..  Training Loss: 0.899..  Valid Loss: 0.689..  Valid Accuracy: 81.221%\n",
            "Epoch: 5/7..  Training Loss: 1.305..  Valid Loss: 0.702..  Valid Accuracy: 80.793%\n",
            "Epoch: 6/7..  Training Loss: 0.162..  Valid Loss: 0.745..  Valid Accuracy: 77.683%\n",
            "Epoch: 6/7..  Training Loss: 1.233..  Valid Loss: 0.614..  Valid Accuracy: 83.144%\n",
            "Epoch: 6/7..  Training Loss: 1.263..  Valid Loss: 0.627..  Valid Accuracy: 82.303%\n",
            "Epoch: 7/7..  Training Loss: 0.698..  Valid Loss: 0.678..  Valid Accuracy: 81.433%\n",
            "Epoch: 7/7..  Training Loss: 1.242..  Valid Loss: 0.616..  Valid Accuracy: 82.683%\n",
            "Epoch: 7/7..  Training Loss: 1.203..  Valid Loss: 0.627..  Valid Accuracy: 83.264%\n"
          ]
        }
      ],
      "source": [
        "epochs = 7\n",
        "print_every = 40\n",
        "steps = 0\n",
        "device = 'cpu'\n",
        "for e in range (epochs):\n",
        "    running_loss = 0\n",
        "    for ii, (inputs, labels) in enumerate (train_loader):\n",
        "        steps += 1\n",
        "\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        #inputs, labels = inputs.to('cuda'), labels.to('cuda')\n",
        "\n",
        "        optimizer.zero_grad () \n",
        "\n",
        "        # Forward and backward passes\n",
        "        outputs = model.forward (inputs) # output\n",
        "        loss = criterion (outputs, labels) #loss\n",
        "        loss.backward ()\n",
        "        optimizer.step () \n",
        "        running_loss += loss.item () \n",
        "\n",
        "        if steps % print_every == 0:\n",
        "            model.eval () \n",
        "\n",
        "            # Turn off gradients for validation, saves memory and computations\n",
        "            with torch.no_grad():\n",
        "                valid_loss, accuracy = validation(model, valid_loader, criterion)\n",
        "\n",
        "            print(\"Epoch: {}/{}.. \".format(e+1, epochs),\n",
        "                  \"Training Loss: {:.3f}.. \".format(running_loss/print_every),\n",
        "                  \"Valid Loss: {:.3f}.. \".format(valid_loss/len(valid_loader)),\n",
        "                  \"Valid Accuracy: {:.3f}%\".format(accuracy/len(valid_loader)*100))\n",
        "\n",
        "            running_loss = 0\n",
        "\n",
        "            # Make sure training is back on\n",
        "            model.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6uuvTRsHEIU",
        "outputId": "d6fed121-a572-4bc1-8dcf-8605e536bbb1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi -L"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqL5WG_xHHkc",
        "outputId": "da3b49e4-58a5-4335-e56e-80e1b30a8ff2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r0% [Working]\r            \rHit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease\n",
            "Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Get:3 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [119 kB]\n",
            "Get:5 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,626 B]\n",
            "Hit:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
            "Hit:7 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n",
            "Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [1,827 kB]\n",
            "Hit:9 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Hit:10 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Hit:11 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [1,738 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,343 kB]\n",
            "Fetched 5,141 kB in 2s (3,163 kB/s)\n",
            "Reading package lists... Done\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "nvidia-driver-470 is already the newest version (470.223.02-0ubuntu0.22.04.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 40 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!sudo apt-get update\n",
        "!sudo apt-get install nvidia-driver-470"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "zW1WnDvMIE9F",
        "outputId": "009fa003-85b7-4a43-ccf9-5a314ce717cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in links: https://download.pytorch.org/whl/cu113/torch_stable.html\n",
            "Collecting torch==1.12.1+cu113\n",
            "  Downloading https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl (1837.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 GB\u001b[0m \u001b[31m585.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.13.1+cu113\n",
            "  Downloading https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl (23.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.4/23.4 MB\u001b[0m \u001b[31m56.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchaudio==0.12.1\n",
            "  Downloading https://download.pytorch.org/whl/cu113/torchaudio-0.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl (3.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m84.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==1.12.1+cu113) (4.9.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision==0.13.1+cu113) (1.25.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision==0.13.1+cu113) (2.31.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision==0.13.1+cu113) (9.4.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.13.1+cu113) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.13.1+cu113) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.13.1+cu113) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.13.1+cu113) (2024.2.2)\n",
            "Installing collected packages: torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.2+cu121\n",
            "    Uninstalling torch-2.1.2+cu121:\n",
            "      Successfully uninstalled torch-2.1.2+cu121\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.16.2+cu121\n",
            "    Uninstalling torchvision-0.16.2+cu121:\n",
            "      Successfully uninstalled torchvision-0.16.2+cu121\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 2.1.2+cu121\n",
            "    Uninstalling torchaudio-2.1.2+cu121:\n",
            "      Successfully uninstalled torchaudio-2.1.2+cu121\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 1.12.1+cu113 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 1.12.1+cu113 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-1.12.1+cu113 torchaudio-0.12.1+cu113 torchvision-0.13.1+cu113\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "torch",
                  "torchgen",
                  "torchvision"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eZi7pabXiLX",
        "outputId": "cf11b86c-1808-4674-f2a2-4562f81bc048"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on test images: 71 %\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "819"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ts_correct = 0\n",
        "ts_total = 0\n",
        "\n",
        "with torch.no_grad ():\n",
        "    for data in test_loader:\n",
        "        inputs, labels = data\n",
        "        inputs, labels = inputs.to('cpu'), labels.to('cpu')\n",
        "        outputs = model (inputs)\n",
        "        _, predicted = torch.max (outputs.data,1)\n",
        "        ts_total += labels.size (0)\n",
        "        ts_correct += (predicted == labels).sum().item()\n",
        "\n",
        "print('Accuracy of the network on test images: %d %%' % (100 * ts_correct / ts_total))\n",
        "ts_total"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaN_urFzXzfB",
        "outputId": "aaef9af3-b637-441e-8177-7b6d7a34bde6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('features.0.weight',\n",
              "              tensor([[[[ 1.1864e-01,  9.4069e-02,  9.5435e-02,  ...,  5.5822e-02,\n",
              "                          2.1575e-02,  4.9963e-02],\n",
              "                        [ 7.4882e-02,  3.8940e-02,  5.2979e-02,  ...,  2.5709e-02,\n",
              "                         -1.1299e-02,  4.1590e-03],\n",
              "                        [ 7.5425e-02,  3.8779e-02,  5.4930e-02,  ...,  4.3596e-02,\n",
              "                          1.0225e-02,  1.3251e-02],\n",
              "                        ...,\n",
              "                        [ 9.3155e-02,  1.0374e-01,  6.7547e-02,  ..., -2.0277e-01,\n",
              "                         -1.2839e-01, -1.1220e-01],\n",
              "                        [ 4.3544e-02,  6.4916e-02,  3.6164e-02,  ..., -2.0248e-01,\n",
              "                         -1.1376e-01, -1.0719e-01],\n",
              "                        [ 4.7369e-02,  6.2543e-02,  2.4758e-02,  ..., -1.1844e-01,\n",
              "                         -9.5567e-02, -8.3890e-02]],\n",
              "              \n",
              "                       [[-7.2634e-02, -5.7996e-02, -8.0661e-02,  ..., -6.0304e-04,\n",
              "                         -2.5309e-02,  2.5471e-02],\n",
              "                        [-6.9042e-02, -6.7562e-02, -7.6367e-02,  ..., -3.9616e-03,\n",
              "                         -3.0402e-02,  1.0477e-02],\n",
              "                        [-9.9517e-02, -8.5592e-02, -1.0521e-01,  ..., -2.6587e-02,\n",
              "                         -2.2777e-02,  6.6451e-03],\n",
              "                        ...,\n",
              "                        [-1.5121e-01, -8.8735e-02, -9.6737e-02,  ...,  3.0853e-01,\n",
              "                          1.8096e-01,  8.4297e-02],\n",
              "                        [-1.4309e-01, -7.5710e-02, -7.2215e-02,  ...,  2.0417e-01,\n",
              "                          1.6447e-01,  9.5166e-02],\n",
              "                        [-8.5925e-02, -4.0134e-02, -5.1491e-02,  ...,  1.6352e-01,\n",
              "                          1.4822e-01,  1.0196e-01]],\n",
              "              \n",
              "                       [[-2.3596e-02, -2.1258e-03, -2.7761e-02,  ...,  3.9940e-02,\n",
              "                         -7.1123e-03,  3.2207e-02],\n",
              "                        [ 2.5705e-04,  2.2468e-02,  8.9070e-03,  ...,  1.8771e-02,\n",
              "                         -1.4155e-02,  1.8275e-02],\n",
              "                        [ 5.4084e-03,  2.9397e-02,  3.3051e-04,  ...,  1.2054e-02,\n",
              "                         -2.5237e-03,  8.3515e-03],\n",
              "                        ...,\n",
              "                        [-6.2826e-02, -1.1655e-02, -6.2080e-02,  ...,  1.0332e-01,\n",
              "                         -9.4987e-03, -7.9570e-02],\n",
              "                        [-4.5691e-02,  3.3726e-03, -3.9632e-02,  ..., -2.6448e-02,\n",
              "                         -3.3500e-02, -7.6398e-02],\n",
              "                        [-1.8700e-02,  1.1365e-02, -3.9671e-02,  ..., -6.8563e-02,\n",
              "                         -4.1289e-02, -5.5473e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.9950e-03,  2.9262e-03,  4.8212e-02,  ...,  6.1402e-02,\n",
              "                          2.6121e-02,  1.9558e-02],\n",
              "                        [-1.2579e-02, -4.8879e-03,  1.8490e-02,  ...,  5.3881e-02,\n",
              "                          1.6377e-02,  2.3768e-02],\n",
              "                        [ 3.6561e-03, -7.7510e-04,  2.6360e-02,  ..., -2.5849e-02,\n",
              "                         -6.1798e-02,  2.6103e-02],\n",
              "                        ...,\n",
              "                        [-1.0812e-02, -4.6008e-03,  1.5122e-02,  ...,  2.9561e-02,\n",
              "                          5.3272e-03,  6.8561e-02],\n",
              "                        [ 2.7364e-04, -1.4850e-02,  7.8180e-03,  ...,  2.7172e-02,\n",
              "                         -1.8095e-02,  5.2485e-02],\n",
              "                        [-5.2470e-02, -4.6578e-02, -1.0951e-02,  ...,  4.3038e-03,\n",
              "                         -2.6379e-03,  1.4406e-02]],\n",
              "              \n",
              "                       [[ 2.3965e-02,  2.2740e-02,  5.7586e-03,  ...,  7.2087e-03,\n",
              "                         -2.4652e-02,  4.4658e-02],\n",
              "                        [ 2.6914e-02,  4.4892e-02, -1.0872e-03,  ...,  4.4243e-02,\n",
              "                         -2.1168e-02,  6.4538e-02],\n",
              "                        [ 1.2421e-02,  1.0247e-02, -4.1554e-02,  ..., -1.2134e-01,\n",
              "                         -1.6294e-01,  2.6266e-02],\n",
              "                        ...,\n",
              "                        [ 3.5926e-02,  5.3235e-02,  1.1016e-02,  ...,  1.2710e-02,\n",
              "                         -2.9737e-02,  8.5926e-02],\n",
              "                        [ 1.5623e-02,  2.1743e-02, -8.2941e-03,  ..., -3.2744e-03,\n",
              "                         -5.4099e-02,  5.7634e-02],\n",
              "                        [ 7.5254e-02,  8.7784e-02,  5.5804e-02,  ...,  5.2849e-02,\n",
              "                          1.0612e-02,  9.3531e-02]],\n",
              "              \n",
              "                       [[-3.6488e-02,  6.6332e-03, -3.9035e-02,  ..., -1.5678e-02,\n",
              "                         -7.9994e-02, -8.8658e-04],\n",
              "                        [-5.1740e-03,  5.7395e-02,  8.9841e-03,  ...,  7.4166e-02,\n",
              "                         -3.1792e-03,  4.2777e-02],\n",
              "                        [-7.9446e-02, -2.2924e-02, -7.3370e-02,  ..., -5.6738e-02,\n",
              "                         -1.2923e-01,  1.8896e-02],\n",
              "                        ...,\n",
              "                        [-3.9394e-02,  3.0981e-02, -2.7901e-02,  ..., -1.6774e-02,\n",
              "                         -1.0236e-01,  4.0128e-02],\n",
              "                        [-6.0751e-02, -2.3034e-02, -7.6838e-02,  ..., -7.9069e-02,\n",
              "                         -1.6195e-01, -1.3746e-02],\n",
              "                        [ 7.9522e-03,  4.6969e-02, -1.2460e-02,  ..., -4.6956e-02,\n",
              "                         -1.0082e-01,  1.9832e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-5.1702e-02,  1.3825e-02,  9.0514e-03,  ..., -9.6401e-02,\n",
              "                         -1.1277e-01, -2.1596e-01],\n",
              "                        [-9.0091e-02, -1.3136e-02, -3.2812e-02,  ..., -7.5263e-02,\n",
              "                         -1.4803e-01, -2.9966e-01],\n",
              "                        [-1.3155e-01, -4.2686e-02, -4.7744e-02,  ...,  2.1429e-01,\n",
              "                          3.2543e-02, -1.7151e-01],\n",
              "                        ...,\n",
              "                        [-1.0621e-01, -9.7966e-02, -2.5551e-01,  ...,  1.2277e-01,\n",
              "                          1.9287e-01,  1.2671e-01],\n",
              "                        [-8.0761e-02, -6.1498e-02, -2.2312e-01,  ...,  3.5376e-02,\n",
              "                          1.0532e-01,  1.0669e-01],\n",
              "                        [ 3.8186e-02,  4.9957e-02, -1.2802e-01,  ..., -3.2927e-02,\n",
              "                          1.8685e-02,  4.7146e-02]],\n",
              "              \n",
              "                       [[ 3.9013e-02,  6.4311e-03, -3.1710e-03,  ..., -2.1245e-02,\n",
              "                          4.0516e-02,  1.1092e-01],\n",
              "                        [ 6.5689e-02,  2.2132e-02,  6.6539e-03,  ..., -3.9448e-02,\n",
              "                          2.7749e-02,  1.1404e-01],\n",
              "                        [ 7.7954e-02,  4.0220e-02,  1.4047e-02,  ..., -1.5417e-01,\n",
              "                         -9.2291e-02,  3.4460e-02],\n",
              "                        ...,\n",
              "                        [ 1.2836e-01,  9.4449e-02,  1.4659e-01,  ..., -6.0067e-02,\n",
              "                         -9.0891e-02, -6.1129e-02],\n",
              "                        [ 1.2683e-01,  1.0044e-01,  1.3754e-01,  ..., -2.2507e-02,\n",
              "                         -6.6664e-02, -1.9906e-02],\n",
              "                        [ 8.0509e-02,  7.8203e-02,  9.8934e-02,  ...,  9.2865e-03,\n",
              "                         -3.4635e-02, -1.2395e-02]],\n",
              "              \n",
              "                       [[ 1.1535e-02, -2.6993e-02,  1.4820e-02,  ...,  9.4833e-02,\n",
              "                          1.2044e-01,  1.1027e-01],\n",
              "                        [ 9.2629e-03, -2.6680e-02,  1.2218e-02,  ...,  8.7219e-02,\n",
              "                          1.5435e-01,  1.8049e-01],\n",
              "                        [ 6.9946e-02,  1.3250e-02,  4.8007e-02,  ..., -5.6851e-02,\n",
              "                          3.2596e-02,  1.6812e-01],\n",
              "                        ...,\n",
              "                        [-1.2187e-02, -3.3265e-02,  1.1284e-01,  ..., -6.7740e-02,\n",
              "                         -1.0240e-01, -7.6188e-02],\n",
              "                        [-6.0069e-03, -2.8631e-02,  1.1643e-01,  ..., -6.7597e-03,\n",
              "                         -4.3772e-02, -3.1101e-02],\n",
              "                        [-1.3355e-01, -1.4825e-01, -1.0060e-03,  ...,  1.8809e-02,\n",
              "                         -6.4637e-03, -2.7061e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 9.0948e-03,  1.4823e-02,  4.7374e-03,  ...,  1.5540e-02,\n",
              "                         -5.8369e-04, -1.9922e-02],\n",
              "                        [ 2.8962e-04,  2.1229e-02, -1.3210e-02,  ...,  2.4388e-03,\n",
              "                         -5.8485e-03, -2.0373e-02],\n",
              "                        [-1.1050e-02,  1.0094e-02, -2.9625e-02,  ..., -1.4471e-02,\n",
              "                         -1.7187e-02, -3.0534e-02],\n",
              "                        ...,\n",
              "                        [ 1.0013e-01,  9.1407e-02,  1.3077e-01,  ...,  1.5798e-01,\n",
              "                          9.0361e-02,  7.8365e-02],\n",
              "                        [ 1.1610e-01,  8.1846e-02,  8.2892e-02,  ..., -6.0174e-02,\n",
              "                         -6.9412e-02, -5.0151e-02],\n",
              "                        [-1.0564e-01, -1.1848e-01, -1.7681e-01,  ..., -2.0837e-01,\n",
              "                         -1.8036e-01, -1.6691e-01]],\n",
              "              \n",
              "                       [[-1.1495e-02,  2.4917e-03, -8.2400e-03,  ..., -7.5865e-03,\n",
              "                         -1.7387e-02, -1.7026e-02],\n",
              "                        [-2.7461e-03, -1.1415e-02, -6.0670e-03,  ..., -2.8248e-02,\n",
              "                         -2.2555e-02, -2.2559e-02],\n",
              "                        [-8.4246e-03, -2.2378e-03, -3.5825e-02,  ..., -1.8462e-02,\n",
              "                         -1.9795e-02, -2.4990e-02],\n",
              "                        ...,\n",
              "                        [ 1.2956e-01,  9.8057e-02,  1.4888e-01,  ...,  1.5655e-01,\n",
              "                          7.9547e-02,  9.6928e-02],\n",
              "                        [ 1.6080e-01,  1.0522e-01,  1.0264e-01,  ..., -6.5226e-02,\n",
              "                         -6.4314e-02, -3.9066e-02],\n",
              "                        [-1.2880e-01, -1.4656e-01, -1.9475e-01,  ..., -2.4177e-01,\n",
              "                         -2.0277e-01, -1.9324e-01]],\n",
              "              \n",
              "                       [[-5.4209e-03, -1.7648e-03,  4.3163e-03,  ...,  9.8182e-03,\n",
              "                          5.2347e-03,  6.3336e-03],\n",
              "                        [ 9.3478e-03,  1.5920e-03, -2.1660e-03,  ...,  7.1244e-03,\n",
              "                          9.3521e-04, -5.7647e-03],\n",
              "                        [-1.3668e-03,  3.8899e-03, -7.7412e-03,  ...,  3.0651e-03,\n",
              "                          1.4989e-02, -8.1730e-03],\n",
              "                        ...,\n",
              "                        [ 4.3937e-02,  7.9544e-04,  6.0613e-02,  ...,  7.4787e-02,\n",
              "                          4.3960e-02,  5.6071e-02],\n",
              "                        [ 1.0077e-01,  7.5126e-02,  1.0962e-01,  ...,  4.9886e-03,\n",
              "                          1.0789e-02,  1.3402e-02],\n",
              "                        [-8.8773e-02, -7.2790e-02, -9.2907e-02,  ..., -6.6530e-02,\n",
              "                         -3.8977e-02, -4.8370e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 4.5712e-03,  4.6908e-02, -1.6075e-02,  ...,  7.7790e-03,\n",
              "                         -1.9798e-02,  6.8000e-03],\n",
              "                        [ 6.2769e-02,  4.5108e-02,  4.7187e-02,  ...,  6.0599e-02,\n",
              "                          2.9275e-02,  5.5794e-02],\n",
              "                        [ 3.6748e-03,  1.2952e-02,  1.8988e-05,  ..., -8.3492e-03,\n",
              "                         -1.9689e-03,  8.0830e-03],\n",
              "                        ...,\n",
              "                        [-4.4365e-02, -5.8858e-02, -2.4772e-02,  ..., -2.8423e-02,\n",
              "                         -3.0897e-02, -5.2936e-02],\n",
              "                        [-9.7572e-03, -4.3227e-02,  9.0068e-03,  ..., -4.2596e-02,\n",
              "                         -1.8114e-02, -2.8028e-02],\n",
              "                        [-2.2007e-02, -3.3594e-02,  1.3479e-02,  ..., -4.1530e-02,\n",
              "                         -1.7819e-02, -5.1977e-02]],\n",
              "              \n",
              "                       [[-9.0596e-02, -5.1485e-02, -1.6459e-01,  ..., -1.1970e-01,\n",
              "                         -1.1150e-01, -4.3914e-02],\n",
              "                        [ 1.3835e-02,  2.6007e-02, -1.9440e-02,  ...,  2.3757e-02,\n",
              "                          6.3709e-03,  5.4287e-02],\n",
              "                        [-9.3225e-02, -4.7454e-02, -1.1274e-01,  ..., -8.6459e-02,\n",
              "                         -7.3958e-02, -6.6610e-02],\n",
              "                        ...,\n",
              "                        [ 2.7382e-02,  1.0310e-02,  4.3906e-02,  ...,  2.7094e-02,\n",
              "                          4.4510e-02,  1.5977e-02],\n",
              "                        [ 9.8431e-02,  6.1433e-02,  1.1413e-01,  ...,  9.6398e-02,\n",
              "                          1.0725e-01,  9.5719e-02],\n",
              "                        [-1.5100e-02, -1.1830e-02,  4.8571e-02,  ...,  2.9060e-02,\n",
              "                          5.6323e-02, -2.1631e-03]],\n",
              "              \n",
              "                       [[-1.3693e-01, -7.9341e-02, -2.1245e-01,  ..., -1.3633e-01,\n",
              "                         -1.5123e-01, -6.3938e-02],\n",
              "                        [ 1.5745e-02,  5.1443e-02, -1.8209e-02,  ...,  4.9085e-02,\n",
              "                          1.9585e-02,  7.8095e-02],\n",
              "                        [-1.7127e-01, -8.8734e-02, -1.7467e-01,  ..., -1.4431e-01,\n",
              "                         -1.3364e-01, -1.1878e-01],\n",
              "                        ...,\n",
              "                        [ 5.1982e-02,  1.5912e-02,  7.0009e-02,  ...,  4.4396e-02,\n",
              "                          6.5429e-02,  2.6919e-02],\n",
              "                        [ 1.3144e-01,  9.1333e-02,  1.6228e-01,  ...,  1.4230e-01,\n",
              "                          1.5500e-01,  1.3808e-01],\n",
              "                        [ 3.7818e-03, -2.0365e-02,  7.8663e-02,  ...,  8.6037e-02,\n",
              "                          1.2596e-01,  3.8774e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-9.5081e-02,  5.6581e-02,  1.4016e-01,  ..., -1.4826e-02,\n",
              "                          1.0425e-02, -3.7919e-03],\n",
              "                        [ 6.0412e-02,  1.2531e-01, -1.2366e-01,  ...,  7.8628e-02,\n",
              "                         -1.0564e-02, -2.2228e-02],\n",
              "                        [ 1.0304e-01, -1.3654e-01, -1.9601e-01,  ..., -5.1919e-02,\n",
              "                         -8.3287e-02,  3.9678e-02],\n",
              "                        ...,\n",
              "                        [-1.8598e-01,  1.5479e-02,  3.3912e-01,  ...,  2.7509e-01,\n",
              "                          1.0493e-01, -1.6863e-01],\n",
              "                        [ 6.8278e-02,  1.3989e-01,  1.1350e-02,  ..., -7.8728e-02,\n",
              "                         -2.5001e-01, -8.0072e-02],\n",
              "                        [ 5.1736e-03, -6.8372e-02, -9.1940e-02,  ..., -1.0727e-01,\n",
              "                          8.7973e-02,  1.0139e-01]],\n",
              "              \n",
              "                       [[-9.8760e-02,  6.2635e-02,  1.1495e-01,  ..., -1.3191e-02,\n",
              "                          1.9574e-02, -1.0864e-03],\n",
              "                        [ 8.3206e-02,  1.0300e-01, -1.5419e-01,  ...,  1.0864e-01,\n",
              "                          1.2592e-02, -2.0278e-02],\n",
              "                        [ 1.1248e-01, -1.6928e-01, -1.8876e-01,  ..., -6.3491e-02,\n",
              "                         -9.3101e-02,  4.9006e-02],\n",
              "                        ...,\n",
              "                        [-1.8867e-01,  6.7374e-02,  4.8128e-01,  ...,  3.1371e-01,\n",
              "                          1.6068e-01, -1.3449e-01],\n",
              "                        [ 1.1207e-01,  2.0871e-01,  4.6815e-02,  ..., -7.0456e-03,\n",
              "                         -2.2978e-01, -6.9528e-02],\n",
              "                        [ 1.7255e-02, -9.1683e-02, -1.5943e-01,  ..., -8.0373e-02,\n",
              "                          8.0035e-02,  1.1819e-01]],\n",
              "              \n",
              "                       [[-1.0580e-01,  5.4381e-02,  1.3048e-01,  ..., -3.9546e-02,\n",
              "                          1.1924e-02, -2.7886e-03],\n",
              "                        [ 5.7859e-02,  1.0646e-01, -1.3468e-01,  ...,  1.0645e-01,\n",
              "                          1.8412e-02, -1.3245e-03],\n",
              "                        [ 1.0398e-01, -1.0849e-01, -1.6758e-01,  ..., -4.2554e-02,\n",
              "                         -8.5875e-02,  5.2581e-02],\n",
              "                        ...,\n",
              "                        [-1.8241e-01,  5.4228e-02,  3.9395e-01,  ...,  2.4432e-01,\n",
              "                          1.0216e-01, -1.2876e-01],\n",
              "                        [ 8.5720e-02,  1.8378e-01,  5.0316e-02,  ..., -4.7009e-02,\n",
              "                         -2.1584e-01, -4.2482e-02],\n",
              "                        [ 3.9612e-02, -7.6353e-02, -1.3502e-01,  ..., -4.8630e-02,\n",
              "                          1.0063e-01,  9.0307e-02]]]])),\n",
              "             ('features.0.bias',\n",
              "              tensor([-0.9705, -2.8070, -0.0371, -0.0795, -0.1159,  0.0252, -0.0752, -1.4181,\n",
              "                       1.6454, -0.0990, -0.0161, -0.1282, -0.0658, -0.0345, -0.0743, -1.2977,\n",
              "                      -0.0505,  0.0121, -0.1013, -1.1887, -0.1380, -0.0492, -0.0789, -0.0405,\n",
              "                      -0.0958, -0.0705, -1.9374, -0.0850, -0.1388, -0.1968, -0.1279, -2.0095,\n",
              "                      -0.0476, -0.0604, -0.0351, -0.3843, -2.7823,  0.6605, -0.1655, -2.1293,\n",
              "                       0.0543, -0.0274, -0.1703, -0.0593, -0.4215, -1.9394, -1.2094,  0.0153,\n",
              "                      -0.1081, -0.0248, -0.1503, -1.8516, -0.0928, -0.0177, -0.0700, -0.0582,\n",
              "                      -0.0630, -0.0721, -1.2678, -0.1176, -0.0441, -0.3259,  0.0507, -0.0146])),\n",
              "             ('features.3.weight',\n",
              "              tensor([[[[ 3.6245e-03,  1.4335e-03,  3.7217e-02, -2.0926e-02,  1.8121e-03],\n",
              "                        [ 2.4126e-02, -1.2056e-02,  7.1170e-02, -8.5224e-02,  1.3067e-02],\n",
              "                        [ 2.0966e-02, -1.0623e-01,  2.1572e-02, -6.9547e-02,  3.1583e-02],\n",
              "                        [-8.3392e-03, -3.9020e-02, -4.6621e-02,  2.2133e-02, -1.3252e-03],\n",
              "                        [-1.5370e-02,  8.6569e-03,  3.1479e-02,  1.6698e-02, -4.7130e-03]],\n",
              "              \n",
              "                       [[-5.4573e-03, -1.9087e-02, -3.2424e-02, -2.2006e-02, -1.2120e-02],\n",
              "                        [-7.4972e-03,  3.0946e-02,  3.1899e-02, -7.6327e-03, -1.4720e-02],\n",
              "                        [ 5.4830e-03,  8.0306e-02,  6.1262e-02,  1.3252e-02,  2.6240e-02],\n",
              "                        [-2.6938e-02,  5.9188e-03,  3.8373e-02, -6.3116e-03,  3.9863e-03],\n",
              "                        [ 2.0844e-03, -3.6292e-02, -2.1987e-03, -1.6570e-02,  5.6354e-03]],\n",
              "              \n",
              "                       [[-1.2587e-02, -5.9436e-02, -9.6281e-02,  1.6745e-02,  4.7471e-02],\n",
              "                        [-1.5036e-02, -1.1214e-01, -2.3924e-02,  3.3459e-02,  4.1306e-02],\n",
              "                        [-1.3845e-02,  4.1235e-02,  2.1580e-01,  2.9520e-02, -5.8914e-02],\n",
              "                        [ 1.2300e-02,  1.0087e-01,  2.1916e-02, -1.4008e-01, -3.1255e-02],\n",
              "                        [ 3.7845e-02,  5.2634e-02, -8.0348e-03, -8.1815e-02,  1.4355e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.2711e-02,  2.5915e-02,  4.1726e-02, -1.3706e-02,  1.4752e-03],\n",
              "                        [-1.0794e-02,  1.6434e-02,  8.1224e-02, -6.0144e-02, -5.1132e-02],\n",
              "                        [-1.3769e-02, -5.3764e-02,  1.1054e-01,  3.8881e-03, -5.8872e-02],\n",
              "                        [ 5.1748e-04,  2.3237e-02,  6.1570e-02,  5.5012e-02, -1.8824e-02],\n",
              "                        [-4.0024e-03,  9.4621e-03, -2.7666e-02, -3.6102e-03, -6.4362e-03]],\n",
              "              \n",
              "                       [[-9.5948e-04, -5.7705e-03,  4.5656e-02,  1.4085e-02, -4.3562e-02],\n",
              "                        [-4.4058e-03, -3.2867e-02,  4.1615e-02, -1.0995e-02,  2.3037e-02],\n",
              "                        [ 1.7953e-02,  5.5599e-03, -4.6110e-02, -6.3326e-02,  2.5256e-02],\n",
              "                        [ 5.1608e-03,  4.1085e-02,  2.1804e-02,  1.9433e-02,  3.1380e-02],\n",
              "                        [-2.9532e-02, -3.1130e-03,  5.4939e-02,  3.5771e-02, -3.1513e-03]],\n",
              "              \n",
              "                       [[ 1.7368e-03,  1.5858e-02, -3.9606e-02, -8.6650e-02,  4.2392e-02],\n",
              "                        [ 3.2755e-02,  2.0259e-02,  1.4398e-01, -1.5988e-01, -1.1522e-01],\n",
              "                        [ 1.1646e-02, -1.3281e-01,  2.5051e-01,  1.9387e-01, -1.4720e-01],\n",
              "                        [ 2.9876e-02, -7.6454e-02, -1.3301e-01,  1.2492e-01,  3.0456e-02],\n",
              "                        [ 4.8693e-02,  7.8451e-02, -3.9283e-02,  4.8439e-03,  2.0383e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.1691e-02, -4.7307e-02,  3.3946e-02,  6.8847e-03,  2.6204e-02],\n",
              "                        [ 1.7683e-02, -5.2123e-02, -6.2835e-02, -3.3933e-02,  1.9128e-02],\n",
              "                        [ 9.2291e-03, -2.8800e-02, -1.8724e-01, -1.9551e-02, -2.1082e-02],\n",
              "                        [-1.4346e-02,  5.7543e-02, -4.1899e-02,  6.3682e-03,  4.2324e-04],\n",
              "                        [ 1.2244e-02,  2.3090e-02, -2.2817e-02,  2.3080e-03, -6.4166e-03]],\n",
              "              \n",
              "                       [[ 4.6469e-03,  7.4133e-03, -4.1122e-02,  2.9806e-02, -1.8110e-02],\n",
              "                        [ 9.6099e-03,  3.9877e-02, -4.1848e-02,  6.9251e-03, -2.5908e-02],\n",
              "                        [-1.0007e-02,  5.4747e-02,  2.6736e-02,  7.0483e-03,  2.6949e-02],\n",
              "                        [-9.5527e-03, -3.6075e-02,  1.4897e-02, -1.2595e-02, -1.8202e-02],\n",
              "                        [-1.4465e-04, -6.3873e-02,  5.8016e-02,  2.8762e-02, -9.7704e-04]],\n",
              "              \n",
              "                       [[-4.5600e-02,  9.9321e-02,  2.0830e-02, -1.7641e-02,  4.3317e-03],\n",
              "                        [-1.1306e-01,  1.0613e-01,  1.2299e-01, -1.5599e-02,  1.1943e-02],\n",
              "                        [-8.6221e-02,  5.1575e-02,  2.8114e-01,  3.8475e-03,  3.9631e-02],\n",
              "                        [ 2.7465e-02, -9.6338e-02,  1.6717e-01,  1.7882e-02, -3.1237e-02],\n",
              "                        [ 3.8818e-02, -1.5017e-01,  9.5537e-02,  8.2108e-02, -2.6467e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.1779e-03,  8.2149e-03,  2.2985e-02, -4.9943e-03, -2.2914e-02],\n",
              "                        [ 4.4609e-03,  4.6661e-02,  3.4394e-02,  5.2542e-02, -1.8957e-02],\n",
              "                        [-1.4202e-02,  5.9543e-02,  6.0551e-03,  3.6463e-02,  3.2430e-04],\n",
              "                        [-2.9488e-02,  4.5902e-02,  5.4980e-02, -2.6936e-03,  4.2663e-03],\n",
              "                        [-2.9711e-02, -1.6390e-02,  3.4428e-02, -9.1090e-03, -9.7971e-03]],\n",
              "              \n",
              "                       [[ 2.0002e-02, -4.4658e-03, -2.8359e-02,  7.4180e-03, -5.3517e-03],\n",
              "                        [ 1.3052e-02, -2.9312e-02, -6.5957e-02,  3.1932e-02, -2.3129e-03],\n",
              "                        [ 1.4003e-02, -5.7715e-02, -9.1541e-02,  2.8261e-03,  1.8726e-02],\n",
              "                        [-1.8143e-02, -4.5023e-02, -9.9762e-03,  1.2348e-02, -1.4161e-03],\n",
              "                        [ 3.3575e-03,  1.8285e-02,  1.2383e-02, -2.0430e-02,  2.4573e-02]],\n",
              "              \n",
              "                       [[-5.4195e-03,  4.5804e-04,  1.7469e-02, -2.2458e-03,  1.7530e-03],\n",
              "                        [-1.0281e-02,  3.1367e-02, -2.5485e-02,  1.2870e-02, -7.0466e-04],\n",
              "                        [-6.3459e-03,  7.3701e-02, -2.9366e-02,  4.9803e-02,  2.5816e-02],\n",
              "                        [-1.9934e-02,  3.8506e-02,  3.0833e-02,  2.5688e-03, -4.4185e-03],\n",
              "                        [ 1.8114e-03,  2.3094e-02,  4.6518e-02, -3.3974e-02, -1.1660e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.0163e-02, -3.7849e-02, -4.6664e-02, -9.7829e-03,  3.7624e-02],\n",
              "                        [ 1.4684e-01, -6.4947e-02, -3.7485e-02, -9.2710e-03,  2.3640e-02],\n",
              "                        [ 1.8563e-02, -3.5328e-02, -9.6485e-03,  1.4210e-02,  2.7077e-02],\n",
              "                        [-6.7717e-03,  2.8039e-03,  4.6860e-03,  4.7567e-03, -6.6633e-03],\n",
              "                        [ 2.2155e-02, -1.2387e-03,  3.1133e-02,  3.1837e-02,  2.4495e-03]],\n",
              "              \n",
              "                       [[ 2.2964e-02, -3.2592e-02, -1.7921e-02,  5.2390e-03,  3.7561e-03],\n",
              "                        [ 3.8685e-02, -8.8764e-02, -5.3735e-02, -2.4427e-02,  1.7369e-02],\n",
              "                        [ 5.5512e-02, -7.1237e-02, -6.5386e-02, -3.7746e-02,  7.1429e-03],\n",
              "                        [ 3.1539e-02, -6.1858e-02, -7.4455e-02, -3.4278e-02, -9.2922e-03],\n",
              "                        [ 4.5122e-02, -8.4307e-03, -2.0509e-02, -1.4743e-02,  1.0508e-03]],\n",
              "              \n",
              "                       [[-9.5345e-03, -2.3179e-02, -1.4264e-02, -6.7151e-03, -2.8285e-02],\n",
              "                        [ 1.6202e-01, -2.8234e-02, -2.4645e-02, -1.0152e-02, -2.4914e-02],\n",
              "                        [ 8.9119e-02, -2.6843e-02, -3.7465e-02, -7.5062e-03, -1.9929e-03],\n",
              "                        [-4.5296e-02, -1.7844e-02, -5.1039e-03,  1.7445e-02,  1.3994e-02],\n",
              "                        [-2.0449e-02, -8.8112e-04,  1.3139e-02,  1.8497e-03, -5.4345e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.5179e-02,  8.3908e-03, -3.4752e-03, -6.7056e-03,  9.4571e-03],\n",
              "                        [ 1.4773e-01,  1.8297e-02, -8.2707e-03, -2.6735e-03, -2.9689e-03],\n",
              "                        [ 1.8945e-02, -2.4611e-02, -2.5792e-02,  5.7622e-03,  1.5490e-02],\n",
              "                        [ 1.0744e-02, -2.2880e-03,  1.3852e-02, -3.0690e-03, -1.4038e-03],\n",
              "                        [ 3.5738e-03, -1.7099e-02, -2.0962e-03, -5.2738e-04, -1.1953e-02]],\n",
              "              \n",
              "                       [[-1.8036e-02,  5.5999e-03, -4.3933e-03, -6.4853e-03,  1.1134e-02],\n",
              "                        [ 8.3012e-02, -8.5719e-02, -2.3690e-02,  2.7197e-02,  1.5338e-02],\n",
              "                        [ 1.6264e-02, -5.0213e-02, -2.9401e-02, -6.0407e-04,  1.5086e-02],\n",
              "                        [ 1.2260e-02,  6.5707e-03, -2.8775e-03, -2.0055e-02,  4.6619e-03],\n",
              "                        [ 1.4575e-02, -9.5385e-03, -2.4609e-02, -2.4521e-03,  1.0301e-03]],\n",
              "              \n",
              "                       [[-1.7248e-02,  7.3069e-03,  1.0429e-02, -1.4917e-02, -6.8504e-03],\n",
              "                        [ 6.2874e-02, -3.2599e-02,  5.7802e-03,  1.4551e-02, -6.1276e-03],\n",
              "                        [ 7.3852e-03, -6.0841e-02, -1.0494e-02,  3.6026e-03, -1.4746e-02],\n",
              "                        [-1.7262e-02,  1.5815e-03, -2.3888e-02, -9.7895e-03, -7.4106e-03],\n",
              "                        [ 6.9352e-03,  7.2060e-03, -1.9073e-02, -6.7136e-03,  6.1392e-03]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 1.7579e-02,  1.0168e-02,  4.4818e-02, -2.6664e-03, -2.2669e-02],\n",
              "                        [-1.5147e-02, -3.2701e-02,  1.4564e-02, -1.2843e-02, -3.2623e-02],\n",
              "                        [-3.0235e-02, -3.2706e-02, -3.4020e-02, -2.0842e-02, -3.2697e-02],\n",
              "                        [-4.2032e-03, -3.4967e-03, -1.0389e-02, -9.6090e-03, -3.1508e-03],\n",
              "                        [ 1.1118e-02,  8.8342e-03,  1.8196e-02,  7.5631e-03,  1.5709e-03]],\n",
              "              \n",
              "                       [[-9.1671e-03,  1.0798e-03, -2.4020e-02, -1.7457e-02, -7.8634e-03],\n",
              "                        [-1.5174e-02,  1.8126e-02,  1.3363e-02,  7.1205e-02,  5.8289e-02],\n",
              "                        [-1.0982e-02,  1.5266e-02, -2.4875e-02, -4.0705e-03, -2.4844e-02],\n",
              "                        [ 2.7485e-03,  1.6571e-02, -3.2608e-02, -5.6266e-02, -3.9944e-02],\n",
              "                        [-4.9014e-04,  1.9889e-02, -3.3673e-03, -2.2187e-02, -9.5482e-03]],\n",
              "              \n",
              "                       [[-2.6544e-02, -4.6466e-02, -1.1130e-01, -6.3725e-02,  2.4153e-02],\n",
              "                        [-3.1451e-02, -4.6735e-02, -5.0069e-02,  3.6642e-02,  8.7667e-02],\n",
              "                        [ 2.4192e-02,  2.9334e-02,  5.9852e-02,  4.2028e-02, -2.5984e-02],\n",
              "                        [ 2.5426e-02,  1.8227e-02,  4.0526e-02, -8.6688e-03, -6.0653e-02],\n",
              "                        [ 1.2664e-02,  1.5354e-02,  3.3492e-02,  1.3319e-02, -2.3908e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.5345e-02, -2.7450e-02, -3.7116e-02,  4.8070e-02,  1.0182e-02],\n",
              "                        [-5.4621e-02, -6.3059e-02, -5.1834e-02,  6.4004e-02,  2.5790e-02],\n",
              "                        [ 1.6780e-02,  1.3105e-02, -3.9535e-02, -1.2138e-03,  9.2518e-03],\n",
              "                        [ 1.4997e-02,  1.2259e-02, -2.3395e-02, -2.8443e-02,  2.4942e-02],\n",
              "                        [-4.0684e-03,  1.4466e-02,  6.2136e-03, -2.2554e-02, -2.0291e-03]],\n",
              "              \n",
              "                       [[-3.5230e-02,  8.0857e-03,  2.3528e-02, -1.0179e-02, -2.1804e-03],\n",
              "                        [-3.4595e-02,  3.9871e-03,  1.3592e-02, -7.9822e-02, -5.2531e-02],\n",
              "                        [-3.2725e-02,  3.4170e-02,  2.6620e-02, -2.4204e-03,  2.4924e-02],\n",
              "                        [ 1.8057e-02,  4.6391e-03, -1.2008e-02,  8.8407e-03,  1.9375e-02],\n",
              "                        [-2.9099e-02,  1.2933e-04, -1.7477e-02,  4.3010e-02,  6.6167e-02]],\n",
              "              \n",
              "                       [[ 4.6078e-02,  2.8413e-02, -2.6394e-02,  4.7513e-03, -7.6480e-02],\n",
              "                        [ 7.5960e-02,  4.4307e-02, -5.8771e-02,  1.7854e-02,  9.6082e-02],\n",
              "                        [ 5.8415e-02,  3.7583e-02, -6.7510e-02, -1.0312e-01, -2.3266e-02],\n",
              "                        [ 4.3488e-02,  6.0671e-02,  6.2699e-02,  3.7197e-03, -5.8098e-02],\n",
              "                        [ 7.3890e-05,  2.7182e-02,  4.3226e-02,  3.1200e-02, -5.9276e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.5983e-02, -2.5757e-02, -2.7917e-02, -6.0154e-02, -2.1587e-02],\n",
              "                        [ 5.8470e-03, -4.0597e-02, -9.7084e-02, -1.1321e-01, -4.5251e-02],\n",
              "                        [-3.3056e-02, -1.7874e-02, -9.1402e-02, -1.0827e-01, -5.3423e-02],\n",
              "                        [-1.4856e-02, -3.3450e-03, -2.7355e-02, -4.8538e-02, -4.1184e-02],\n",
              "                        [ 9.0377e-04, -1.8180e-02, -3.1040e-02, -1.1854e-02, -2.6462e-02]],\n",
              "              \n",
              "                       [[ 7.2521e-02,  1.6407e-02, -1.5754e-01, -8.8864e-02, -2.1638e-02],\n",
              "                        [ 9.2281e-02,  8.5642e-02, -6.8582e-02, -7.8368e-02, -9.3343e-02],\n",
              "                        [ 1.1667e-01,  1.4296e-01,  1.4621e-02, -6.6599e-02, -1.8653e-01],\n",
              "                        [ 4.0418e-02,  8.7898e-02,  4.2521e-02, -8.9029e-03, -1.2440e-01],\n",
              "                        [ 2.0246e-02,  6.5828e-02,  9.6516e-02,  5.8696e-02, -7.2705e-02]],\n",
              "              \n",
              "                       [[-3.7801e-02, -1.1232e-02, -8.1927e-03,  1.2744e-02,  6.8491e-02],\n",
              "                        [-4.4532e-03, -9.6229e-03, -3.2608e-02,  2.9350e-03,  3.7486e-02],\n",
              "                        [ 3.3585e-02,  4.8458e-02,  2.6881e-02, -6.1178e-02, -2.1019e-02],\n",
              "                        [-1.9669e-02,  6.9724e-03,  4.2018e-02, -4.3931e-02, -7.6382e-02],\n",
              "                        [ 8.3337e-03,  2.5425e-03,  2.8063e-02, -3.4867e-02, -8.6687e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.2673e-02, -1.5327e-02,  1.6517e-02,  3.3334e-02,  1.3038e-02],\n",
              "                        [ 1.6805e-03, -1.8921e-02, -3.6130e-02,  3.2359e-02,  5.0130e-02],\n",
              "                        [-1.3607e-02, -3.3703e-02, -7.9256e-02, -5.1558e-03,  6.5082e-02],\n",
              "                        [-1.3499e-03,  1.9183e-02, -1.2600e-03, -1.5375e-02,  9.9117e-03],\n",
              "                        [-8.6383e-03, -6.4556e-03,  4.4472e-03, -7.7968e-04,  2.6764e-02]],\n",
              "              \n",
              "                       [[-3.1357e-02, -6.5565e-02, -4.5553e-02,  2.9694e-02,  5.0477e-02],\n",
              "                        [ 2.6388e-02, -6.5116e-03, -1.4299e-01, -2.9214e-02,  1.2134e-03],\n",
              "                        [ 5.2430e-02,  6.1108e-02, -2.8583e-02, -1.0998e-01, -3.4642e-02],\n",
              "                        [ 1.6018e-02,  3.1906e-02,  7.7014e-02, -1.1630e-02, -5.6960e-02],\n",
              "                        [ 4.9326e-02, -1.5627e-02,  4.2784e-02,  3.7805e-02, -2.6815e-02]],\n",
              "              \n",
              "                       [[ 9.0381e-03,  1.6271e-03,  9.9757e-03,  2.6084e-02,  6.8623e-03],\n",
              "                        [ 1.0622e-02, -6.1560e-03, -9.2547e-03,  1.8062e-02,  2.2674e-02],\n",
              "                        [ 2.8991e-02,  1.2231e-04, -1.6760e-02, -1.9807e-03,  3.1453e-02],\n",
              "                        [ 1.8964e-02,  2.8631e-02,  5.8559e-04, -1.0603e-02,  1.1077e-02],\n",
              "                        [ 5.4998e-03, -6.3534e-03,  2.9878e-03, -2.7331e-02, -2.4437e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 7.0352e-03, -2.0708e-02, -1.9059e-02, -2.8221e-03,  9.0732e-03],\n",
              "                        [-2.2202e-02, -3.6482e-02, -3.3435e-02, -2.0294e-02, -1.1695e-02],\n",
              "                        [-7.7194e-03, -3.2950e-02, -5.5740e-02, -3.5089e-02, -1.5053e-02],\n",
              "                        [-1.2823e-02, -3.5408e-02, -4.2076e-02, -1.6718e-02, -1.8323e-02],\n",
              "                        [-3.9433e-03,  5.0854e-03, -2.3092e-03,  1.1569e-02,  1.1675e-02]],\n",
              "              \n",
              "                       [[ 1.8536e-02,  2.3997e-02,  1.8607e-02, -2.1316e-03,  3.0036e-02],\n",
              "                        [-9.1898e-03, -2.0234e-02, -2.0921e-02, -1.5293e-02, -4.1515e-03],\n",
              "                        [-2.5399e-02, -3.6147e-02,  1.1215e-02, -1.3978e-02,  1.0484e-02],\n",
              "                        [-1.7011e-03, -1.7085e-02,  1.9866e-02,  6.1436e-03,  4.9555e-03],\n",
              "                        [ 1.0626e-02,  1.2917e-02,  4.2516e-02,  1.5874e-02,  1.7050e-02]],\n",
              "              \n",
              "                       [[-4.0153e-03,  1.6083e-02,  8.7844e-03, -9.1274e-03,  2.2305e-02],\n",
              "                        [-2.0949e-02, -3.9927e-03, -3.2194e-02, -2.1123e-02, -1.3385e-03],\n",
              "                        [-4.2372e-03,  1.4911e-02,  3.8126e-03, -5.2412e-03,  9.8842e-03],\n",
              "                        [ 1.2249e-02,  3.7495e-03, -3.4932e-03,  6.0049e-03,  1.4841e-02],\n",
              "                        [ 1.3200e-02, -1.9996e-03,  2.0933e-02,  1.6309e-04, -8.3707e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-6.4706e-03, -3.2926e-02, -1.5733e-02, -1.7831e-02,  2.1256e-03],\n",
              "                        [-5.4066e-02, -5.4719e-02, -4.5444e-02, -5.6256e-02, -3.6743e-02],\n",
              "                        [ 2.8305e-02,  2.9538e-02,  4.4844e-02,  2.2081e-02,  3.5385e-02],\n",
              "                        [ 1.6821e-02,  1.6557e-02,  5.4560e-02,  1.7084e-02,  2.3373e-02],\n",
              "                        [-4.0657e-03, -8.0451e-03,  4.4215e-04,  7.5198e-03,  1.0919e-02]],\n",
              "              \n",
              "                       [[ 2.0678e-02,  2.0736e-02,  7.6578e-02,  2.9091e-02,  4.1559e-02],\n",
              "                        [-1.0118e-03,  5.5359e-02,  1.3228e-01,  6.7964e-02,  4.1530e-02],\n",
              "                        [-3.9346e-03,  3.5696e-02,  1.2877e-01,  6.1861e-02,  2.9380e-02],\n",
              "                        [ 2.5981e-02,  3.8598e-02,  1.3250e-01,  3.2665e-02, -1.8039e-02],\n",
              "                        [-1.9937e-02, -1.3913e-02, -1.0549e-02, -1.3653e-02, -2.4947e-03]],\n",
              "              \n",
              "                       [[ 7.0426e-02,  2.1494e-02, -9.1289e-03, -9.1144e-03, -5.7207e-03],\n",
              "                        [ 3.0881e-02, -1.0100e-02, -5.8352e-02, -3.8790e-02, -2.0214e-02],\n",
              "                        [-7.5705e-03, -4.3871e-02, -8.5624e-02, -5.4874e-02, -1.8760e-02],\n",
              "                        [-5.6932e-03, -4.2488e-02, -7.0337e-02, -3.9515e-02,  2.8042e-03],\n",
              "                        [ 4.9093e-02,  8.0856e-03, -2.0497e-02,  1.7022e-02,  2.4330e-02]]]])),\n",
              "             ('features.3.bias',\n",
              "              tensor([-1.0928e-01, -1.7263e-01, -5.1698e-02, -6.5495e-02, -3.9439e-02,\n",
              "                       1.7420e-01, -1.8464e-01,  9.5525e-02, -2.5099e-02, -3.0075e-02,\n",
              "                       1.3654e-01, -3.2269e-01, -1.4834e-01,  1.2790e-01,  4.0678e-03,\n",
              "                      -5.7279e-02, -2.1440e-03,  2.0314e-01,  3.3098e-01,  7.3128e-02,\n",
              "                       4.9798e-01, -4.4765e-02, -8.2151e-02,  1.4895e-02, -4.6373e-02,\n",
              "                       5.7510e-02,  1.9020e-02,  7.4533e-04, -1.8182e-01, -1.2202e-04,\n",
              "                      -1.3162e-01, -1.2743e-01,  5.9956e-03, -4.2740e-01,  1.1648e-01,\n",
              "                      -1.6375e-01, -6.0307e-02, -2.3113e-01,  2.4188e-01, -7.8616e-02,\n",
              "                       2.0777e-02, -2.5044e-02, -4.4685e-02,  2.8326e-02, -1.7459e-01,\n",
              "                       7.1566e-02, -5.7368e-02,  1.2070e-01, -5.8133e-02, -1.7347e-01,\n",
              "                      -1.2782e-01,  5.3739e-02,  4.5314e-02,  9.4975e-02,  1.0401e-02,\n",
              "                       1.3877e-01, -1.3664e-01,  2.4293e-01,  1.4512e-01,  4.8102e-01,\n",
              "                      -1.1793e-01, -1.5927e-01,  1.6019e-01,  2.2559e-01,  9.6043e-02,\n",
              "                       2.9943e-02, -1.7576e-01, -9.5031e-02,  2.3446e-01, -2.4519e-01,\n",
              "                       1.0253e-01, -2.4316e-01, -5.6964e-02, -2.7844e-02, -3.4611e-01,\n",
              "                       4.0740e-02, -2.3085e-01,  1.9122e-01,  1.6288e-01,  2.9599e-01,\n",
              "                      -1.3699e-01,  5.2276e-03, -1.6603e-01, -7.7020e-02, -1.1686e-01,\n",
              "                       6.8327e-02, -9.8369e-02, -9.1281e-02, -1.6490e-01,  6.2494e-02,\n",
              "                       1.0231e-01, -5.7519e-03, -1.7242e-01, -8.5797e-03, -1.2002e-01,\n",
              "                      -1.8632e-01, -5.3833e-02, -6.7391e-02, -1.4795e-01,  2.6208e-02,\n",
              "                       3.9989e-02, -2.1992e-01, -1.4985e-01, -8.3122e-02, -6.7938e-02,\n",
              "                      -7.8998e-02, -4.2145e-02,  3.7165e-01, -1.8347e-01,  6.1246e-02,\n",
              "                      -4.7721e-01, -2.1703e-01, -7.0511e-02, -9.9998e-02, -4.4748e-02,\n",
              "                       8.6560e-02, -1.3605e-01, -1.8263e-01, -3.7893e-02, -2.9278e-01,\n",
              "                      -1.3911e-01,  1.4130e-01,  2.9491e-01,  2.7607e-01, -4.4584e-02,\n",
              "                       3.6733e-01,  8.7795e-02, -2.0914e-02,  2.0941e-01,  4.9935e-01,\n",
              "                       1.4991e-01, -1.3268e-01,  2.1515e-01,  2.3250e-01,  1.4488e-01,\n",
              "                      -8.2328e-02, -3.2715e-02, -1.2761e-01,  1.1177e-01,  3.8759e-01,\n",
              "                      -3.7940e-02, -1.7950e-01,  3.1519e-01, -4.0012e-02,  3.2606e-01,\n",
              "                       1.0944e-02, -4.2019e-01, -1.9330e-01,  4.6443e-02,  1.4548e-02,\n",
              "                       4.1791e-02,  1.4871e-01, -1.5567e-01, -1.6159e-01,  4.9127e-01,\n",
              "                      -1.4576e-01,  1.3040e-01, -3.2479e-02, -3.3374e-02, -3.0722e-01,\n",
              "                       3.0348e-01, -8.2917e-02,  1.5031e-01, -2.1193e-02,  1.7708e-01,\n",
              "                       2.0579e-01,  1.0637e-01, -1.4460e-01, -1.6046e-01, -2.6753e-02,\n",
              "                      -6.7287e-02,  4.8736e-01,  1.7410e-01,  1.8411e-01, -8.9367e-02,\n",
              "                       1.4409e-01,  5.3045e-02, -1.3101e-01, -8.4564e-02, -3.6975e-02,\n",
              "                      -2.3806e-01, -8.0580e-02, -9.7469e-02,  1.4895e-01, -3.0727e-03,\n",
              "                       2.8690e-01,  9.3942e-02,  1.2006e-01, -1.2438e-01,  7.2421e-02,\n",
              "                       1.3842e-02,  7.2982e-02])),\n",
              "             ('features.6.weight',\n",
              "              tensor([[[[ 2.4858e-02,  1.3116e-02,  2.8198e-02],\n",
              "                        [ 4.2541e-02,  5.7339e-02, -6.0905e-03],\n",
              "                        [-4.1912e-03,  9.3096e-03, -1.5442e-02]],\n",
              "              \n",
              "                       [[-3.8954e-03, -7.8586e-02, -5.1808e-02],\n",
              "                        [ 2.6484e-02, -4.9877e-02, -1.7763e-03],\n",
              "                        [ 8.2902e-03, -4.9339e-02,  3.1145e-02]],\n",
              "              \n",
              "                       [[ 1.6455e-02, -1.2150e-02,  1.7428e-02],\n",
              "                        [ 5.2012e-02, -6.7141e-03,  2.7325e-02],\n",
              "                        [ 7.5568e-03, -4.2402e-02, -2.7909e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.7289e-04,  2.4099e-02,  3.6948e-02],\n",
              "                        [-2.4145e-02, -3.5276e-02,  3.6910e-03],\n",
              "                        [ 1.9541e-02, -3.0342e-02, -3.5262e-02]],\n",
              "              \n",
              "                       [[ 1.3239e-02, -1.8624e-02, -5.3330e-02],\n",
              "                        [ 1.7639e-04, -1.4714e-02, -2.2829e-02],\n",
              "                        [-6.7702e-03,  2.3287e-02,  1.3873e-02]],\n",
              "              \n",
              "                       [[ 3.0512e-02,  7.5860e-03,  4.9459e-04],\n",
              "                        [-4.5703e-03, -1.2827e-02, -6.5061e-03],\n",
              "                        [-9.8111e-03, -1.5570e-02,  1.9379e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.3660e-02, -3.9180e-02,  2.0014e-02],\n",
              "                        [ 5.7389e-02,  1.1934e-02,  2.3058e-02],\n",
              "                        [-2.2237e-02, -1.8879e-02, -2.0941e-02]],\n",
              "              \n",
              "                       [[-1.3402e-02,  1.9384e-02,  2.0018e-02],\n",
              "                        [ 2.2285e-03, -1.5328e-02, -2.0604e-02],\n",
              "                        [ 5.2206e-02, -4.0713e-02, -1.6011e-02]],\n",
              "              \n",
              "                       [[ 8.2728e-03, -1.2311e-02, -2.6974e-02],\n",
              "                        [ 2.5519e-03, -5.6930e-03, -5.1476e-02],\n",
              "                        [-2.8822e-02, -8.5134e-02, -6.5895e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.1531e-02,  2.4226e-02,  6.0344e-04],\n",
              "                        [-3.3641e-02,  7.7504e-02, -1.8122e-02],\n",
              "                        [ 2.2435e-02,  6.3408e-02, -1.0774e-03]],\n",
              "              \n",
              "                       [[-1.6893e-02,  5.7512e-03,  3.7639e-02],\n",
              "                        [-1.6503e-02, -3.2492e-03,  3.6293e-02],\n",
              "                        [ 3.2918e-02, -1.2321e-02,  1.4753e-03]],\n",
              "              \n",
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              "                        [-1.3995e-02, -2.4027e-02, -1.2127e-02],\n",
              "                        [ 2.6365e-03, -1.4931e-02, -7.9207e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 8.0889e-03,  9.6816e-03, -1.2593e-02],\n",
              "                        [ 1.1684e-02,  4.7408e-02,  4.9439e-02],\n",
              "                        [-1.1663e-02,  7.7298e-02,  2.3240e-02]],\n",
              "              \n",
              "                       [[ 9.5861e-03, -3.5039e-02, -1.5836e-02],\n",
              "                        [-2.2685e-02,  1.6363e-02,  1.8725e-02],\n",
              "                        [-1.7577e-02, -5.9963e-03, -1.2350e-02]],\n",
              "              \n",
              "                       [[-3.9020e-03, -5.0362e-03,  2.8650e-03],\n",
              "                        [ 2.3688e-02,  3.0239e-03, -5.0350e-02],\n",
              "                        [-3.0330e-02, -2.0917e-02, -9.9929e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.3766e-02, -4.8649e-03,  3.4593e-03],\n",
              "                        [-1.7299e-03,  9.8713e-03,  3.1695e-03],\n",
              "                        [-1.5713e-02,  2.4070e-02,  4.3523e-02]],\n",
              "              \n",
              "                       [[ 1.7398e-02,  3.9648e-02,  5.0599e-02],\n",
              "                        [-1.8082e-02,  2.3186e-02,  7.5082e-02],\n",
              "                        [-1.4652e-02, -1.4806e-03,  2.4491e-02]],\n",
              "              \n",
              "                       [[-3.6486e-03,  1.6976e-02,  9.5258e-03],\n",
              "                        [ 8.3009e-03,  3.0063e-03, -3.5994e-03],\n",
              "                        [-2.0521e-04, -9.6109e-04, -7.6914e-03]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-2.1159e-02,  3.1025e-02, -5.1508e-04],\n",
              "                        [-2.0021e-02, -9.5897e-03,  2.7262e-02],\n",
              "                        [-9.7968e-03, -3.9145e-02,  9.2344e-03]],\n",
              "              \n",
              "                       [[-2.2890e-02, -6.2061e-03, -1.8220e-02],\n",
              "                        [ 2.6104e-02,  1.8208e-02, -3.5678e-02],\n",
              "                        [-1.9037e-02, -2.5568e-02, -3.2400e-03]],\n",
              "              \n",
              "                       [[ 4.5357e-04,  1.1460e-02,  3.0362e-02],\n",
              "                        [ 2.1339e-02,  1.1392e-02,  1.3384e-02],\n",
              "                        [ 2.2492e-02, -1.0258e-02, -5.8449e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-2.7361e-02,  1.4307e-02,  1.8851e-02],\n",
              "                        [ 3.9169e-02, -4.1835e-02, -1.2617e-03],\n",
              "                        [-2.4972e-02, -6.0201e-02,  1.1093e-02]],\n",
              "              \n",
              "                       [[ 1.3269e-02,  2.8252e-02, -2.0582e-02],\n",
              "                        [ 1.2612e-02,  5.3777e-02, -2.9175e-02],\n",
              "                        [ 4.3714e-02,  1.5523e-02, -3.4617e-02]],\n",
              "              \n",
              "                       [[-1.4312e-02, -1.1627e-02, -8.0607e-03],\n",
              "                        [-1.7464e-02,  9.4876e-03, -3.3993e-04],\n",
              "                        [-4.5948e-03,  2.4349e-02,  2.9515e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.3024e-02,  3.7754e-02, -7.9412e-03],\n",
              "                        [ 2.3338e-02, -3.2373e-02, -4.1358e-02],\n",
              "                        [ 6.4814e-02,  2.6370e-02,  4.9119e-02]],\n",
              "              \n",
              "                       [[ 4.7991e-02, -2.4855e-02,  1.2162e-02],\n",
              "                        [-2.7339e-02,  3.9421e-02, -1.8773e-02],\n",
              "                        [-5.8830e-02,  2.6058e-02,  1.4781e-03]],\n",
              "              \n",
              "                       [[ 8.6110e-03, -2.6908e-02,  1.1525e-02],\n",
              "                        [ 5.6151e-02, -1.3285e-02, -7.1971e-03],\n",
              "                        [ 1.1242e-03, -4.7446e-02, -3.6017e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.5633e-02, -8.5117e-03,  6.2245e-02],\n",
              "                        [ 1.0972e-02,  1.9737e-02,  4.8234e-02],\n",
              "                        [-1.1555e-02,  4.3559e-03,  5.2946e-02]],\n",
              "              \n",
              "                       [[-1.0731e-03,  2.2912e-02,  2.2005e-02],\n",
              "                        [ 1.3288e-02,  4.9365e-02,  1.2220e-02],\n",
              "                        [ 1.0511e-02,  1.7318e-02, -1.8007e-02]],\n",
              "              \n",
              "                       [[-2.6693e-02, -1.8063e-02, -2.4829e-02],\n",
              "                        [-3.3943e-02, -1.4893e-03, -1.1168e-02],\n",
              "                        [-1.3225e-02, -4.6098e-03,  3.0331e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-6.0866e-03, -2.5078e-02, -4.7683e-02],\n",
              "                        [ 2.4006e-02,  5.2576e-02, -2.1802e-02],\n",
              "                        [ 2.5606e-02,  1.1889e-02, -2.4519e-03]],\n",
              "              \n",
              "                       [[ 2.5286e-02, -2.8539e-02,  8.4982e-03],\n",
              "                        [ 6.8764e-03, -8.9434e-03,  3.4527e-03],\n",
              "                        [ 2.7720e-02,  1.2911e-02,  1.2079e-02]],\n",
              "              \n",
              "                       [[ 2.8175e-02,  1.3563e-02,  6.9976e-02],\n",
              "                        [ 2.7799e-02, -3.6929e-02,  1.0927e-01],\n",
              "                        [ 2.3212e-02, -3.4578e-02, -4.8821e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-3.3232e-04,  1.7463e-02, -9.6818e-03],\n",
              "                        [ 9.2093e-03,  1.5052e-02,  8.6647e-03],\n",
              "                        [-1.8870e-02, -2.1472e-02, -1.5119e-02]],\n",
              "              \n",
              "                       [[ 4.3916e-02, -5.5188e-05, -7.7297e-03],\n",
              "                        [-8.0442e-03, -3.4422e-02, -2.2207e-02],\n",
              "                        [-1.4105e-02, -2.7909e-02, -2.4631e-02]],\n",
              "              \n",
              "                       [[-2.6292e-02,  1.1666e-02, -1.6261e-02],\n",
              "                        [-5.7774e-02, -5.8361e-02, -4.8310e-02],\n",
              "                        [-4.1695e-02, -6.6255e-02, -5.7636e-02]]]])),\n",
              "             ('features.6.bias',\n",
              "              tensor([ 2.5296e-02,  1.1945e-01, -3.3357e-01,  1.1266e-01, -1.1582e-01,\n",
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              "                       1.2284e-01, -1.2416e-01,  3.0930e-02, -1.1544e-01, -2.8152e-02,\n",
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              "                      -1.3086e-01,  9.2488e-02, -5.3630e-03,  8.9322e-02,  5.3354e-02,\n",
              "                       6.6519e-02,  3.5378e-02,  4.4817e-02, -8.7586e-02,  1.3602e-01,\n",
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              "                      -8.4326e-02, -6.7575e-02, -1.1365e-02,  1.0826e-01, -1.0933e-01,\n",
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              "                       5.0955e-01, -3.2508e-03,  5.4857e-02,  6.7155e-02,  1.0905e-01,\n",
              "                       1.8573e-02, -2.9313e-02,  1.1019e-02, -1.1489e-02])),\n",
              "             ('features.8.weight',\n",
              "              tensor([[[[-0.0020, -0.0081, -0.0114],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0128, -0.0524,  0.0036]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "                        [-0.0541, -0.0190, -0.0386]],\n",
              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
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              "                        [ 0.0153,  0.0184,  0.0001]],\n",
              "              \n",
              "                       [[-0.0324, -0.0163,  0.0237],\n",
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              "                        [-0.0236,  0.0041,  0.0363]],\n",
              "              \n",
              "                       [[-0.0293, -0.0176, -0.0268],\n",
              "                        [-0.0341,  0.0460, -0.0113],\n",
              "                        [ 0.0003,  0.0701,  0.0068]]]])),\n",
              "             ('features.8.bias',\n",
              "              tensor([-6.2908e-02,  1.2599e-01,  2.9910e-01,  1.1226e-01,  2.8529e-01,\n",
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              "                       2.6192e-01,  4.5637e-02, -1.4264e+00,  6.0950e-01,  8.2353e-02,\n",
              "                       9.6382e-02,  1.5679e-01, -2.9053e-01,  1.2642e-01,  2.0995e-01,\n",
              "                       7.2459e-02, -2.7236e-04, -2.2086e-01,  1.1173e-01,  4.8032e-01,\n",
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              "                       1.4878e-01, -8.2103e-03,  1.3206e-01,  2.9920e-01, -1.6474e-01,\n",
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              "                       2.3581e-01, -3.8736e-01, -6.0125e-02,  9.0375e-01, -1.4004e-01,\n",
              "                       5.3197e-01,  2.3184e-01,  2.0689e-01,  2.3384e-01, -3.6393e-01,\n",
              "                       2.2036e-01, -5.4082e-01, -6.4925e-01,  9.8469e-02,  3.4030e-01,\n",
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              "                      -1.3941e-01,  3.3986e-02,  5.0526e-01,  2.3880e-01,  9.0405e-02,\n",
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              "                      -8.3527e-02, -1.4610e-01,  1.8069e-01, -1.7798e-01, -9.7765e-02,\n",
              "                      -6.4475e-03,  8.1148e-01,  2.0052e-01,  3.0710e-01, -2.3601e-01,\n",
              "                       7.3178e-02,  1.1172e-01,  2.8366e-01,  6.6962e-02,  1.6107e-01,\n",
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              "                       4.2133e-02,  1.0998e-01,  4.3315e-01,  1.4762e-01, -2.2403e-02,\n",
              "                       2.1770e-01,  5.9078e-02,  2.9927e-01,  8.4790e-02, -2.8061e-01,\n",
              "                       3.7362e-02, -1.9944e-01, -4.8672e-01,  2.0394e-01,  1.9824e-01,\n",
              "                       1.3828e-01,  3.0206e-02, -2.0721e-01,  7.0921e-01, -1.0912e-01,\n",
              "                       4.8155e-01,  2.9200e-01,  4.7852e-02, -7.9537e-01, -4.4115e-02,\n",
              "                       1.3331e-01, -1.9617e-01,  3.3779e-01,  4.0620e-01, -1.1334e-01,\n",
              "                       5.1684e-02,  7.7361e-02,  2.6791e-01,  3.2820e-01,  1.8926e-01,\n",
              "                      -2.1592e-02,  5.3141e-02, -2.4303e-01,  1.5920e-01,  5.7491e-01,\n",
              "                       7.7323e-02, -7.1903e-03,  3.1149e-01,  3.3197e-02,  2.7632e-01,\n",
              "                       1.4343e-01])),\n",
              "             ('features.10.weight',\n",
              "              tensor([[[[ 4.5008e-03, -7.7347e-03, -1.5003e-02],\n",
              "                        [-3.0285e-02, -4.4114e-02, -1.7563e-02],\n",
              "                        [-1.4331e-02, -3.6714e-02, -5.2003e-02]],\n",
              "              \n",
              "                       [[ 3.3324e-02,  9.8262e-03, -6.4676e-03],\n",
              "                        [ 1.4188e-02,  2.4336e-02,  3.1040e-02],\n",
              "                        [-1.9439e-02,  1.9590e-02, -1.6543e-02]],\n",
              "              \n",
              "                       [[ 3.3328e-05, -6.3628e-04,  1.5421e-02],\n",
              "                        [ 1.1260e-02,  5.7387e-04,  1.7457e-02],\n",
              "                        [ 7.1211e-03,  1.0711e-03,  2.1856e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.6804e-02, -2.1512e-02, -1.0703e-02],\n",
              "                        [-1.0698e-02, -5.3842e-02, -1.9335e-02],\n",
              "                        [ 1.6292e-02,  4.4939e-04,  6.5307e-05]],\n",
              "              \n",
              "                       [[ 1.2114e-03,  1.1926e-03, -1.7091e-03],\n",
              "                        [-1.7243e-02, -2.7680e-02,  1.9918e-05],\n",
              "                        [ 5.2268e-04, -1.6247e-03, -4.3989e-03]],\n",
              "              \n",
              "                       [[ 1.9892e-02,  2.9338e-02,  2.0883e-02],\n",
              "                        [ 1.3522e-02,  3.1604e-02,  1.2854e-02],\n",
              "                        [ 5.2760e-03,  1.2341e-02,  7.5441e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.4208e-02,  2.3224e-03, -2.5112e-02],\n",
              "                        [-7.6076e-03,  9.4883e-03, -4.6517e-03],\n",
              "                        [ 1.3253e-02,  1.1511e-02, -1.4017e-02]],\n",
              "              \n",
              "                       [[ 2.1794e-02,  1.0763e-02,  1.1629e-02],\n",
              "                        [ 4.1082e-02, -7.8142e-03,  1.9052e-02],\n",
              "                        [ 6.6561e-02,  3.6654e-02,  2.0193e-03]],\n",
              "              \n",
              "                       [[-1.8703e-02, -4.9244e-02, -2.9182e-02],\n",
              "                        [-1.5992e-02,  1.6744e-04,  4.5855e-03],\n",
              "                        [-6.2261e-03,  4.1283e-03,  8.5156e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-5.8011e-02, -7.3355e-02, -6.5635e-02],\n",
              "                        [-5.9603e-02, -5.7514e-02, -4.2397e-02],\n",
              "                        [-5.5825e-02, -6.3877e-02, -6.0623e-02]],\n",
              "              \n",
              "                       [[-3.6692e-02, -3.2975e-02, -1.0278e-02],\n",
              "                        [-3.1835e-02, -5.3899e-03, -3.2266e-02],\n",
              "                        [-3.2715e-02, -3.3150e-02, -2.8616e-02]],\n",
              "              \n",
              "                       [[-8.2592e-03, -2.5406e-02, -1.7791e-02],\n",
              "                        [-1.1399e-02, -2.3384e-02, -1.1688e-02],\n",
              "                        [-2.2682e-02, -3.1900e-02, -7.9640e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.8231e-02, -2.9938e-02, -3.0918e-02],\n",
              "                        [ 6.0674e-04, -4.5208e-02, -4.3832e-03],\n",
              "                        [-6.0434e-03, -6.1846e-02, -1.4543e-02]],\n",
              "              \n",
              "                       [[ 1.3111e-02,  1.5979e-02, -5.2871e-03],\n",
              "                        [-1.2694e-02,  1.2434e-03,  2.2471e-02],\n",
              "                        [ 2.1080e-02, -6.7459e-03,  1.9622e-02]],\n",
              "              \n",
              "                       [[ 3.0896e-02, -3.0819e-02,  5.1553e-03],\n",
              "                        [-5.7161e-04, -1.2514e-02,  2.3466e-02],\n",
              "                        [ 2.5366e-02,  3.1077e-02,  2.8808e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-2.0043e-02, -3.3691e-02, -3.3587e-02],\n",
              "                        [-3.1222e-02, -4.3832e-02, -2.9784e-02],\n",
              "                        [-5.5385e-02, -6.6676e-02, -5.1232e-02]],\n",
              "              \n",
              "                       [[-1.8431e-02, -1.0482e-02, -1.3102e-02],\n",
              "                        [ 5.6541e-03, -2.6609e-02,  2.4034e-03],\n",
              "                        [-2.3949e-02,  8.9346e-03,  3.3861e-03]],\n",
              "              \n",
              "                       [[ 5.7008e-03, -1.4837e-02,  1.3501e-03],\n",
              "                        [-5.6762e-04,  6.3385e-03,  5.1770e-03],\n",
              "                        [ 5.5514e-03, -1.4905e-03, -2.3526e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 1.8694e-02, -5.8550e-03,  7.5898e-03],\n",
              "                        [ 5.4762e-03, -5.1852e-02,  5.3048e-03],\n",
              "                        [ 5.4274e-02, -3.1494e-03, -5.8523e-03]],\n",
              "              \n",
              "                       [[ 1.0686e-02, -1.6323e-02,  2.0675e-02],\n",
              "                        [ 1.9367e-03, -3.2638e-02,  9.5754e-04],\n",
              "                        [ 2.1652e-03,  2.7449e-02,  5.5060e-02]],\n",
              "              \n",
              "                       [[ 5.0755e-03, -2.6414e-02,  1.8585e-02],\n",
              "                        [-2.5866e-03,  1.2334e-03,  4.8944e-03],\n",
              "                        [ 5.3379e-04, -8.6257e-03, -2.3603e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.3444e-02,  9.6432e-03,  1.7831e-02],\n",
              "                        [-1.8331e-02, -5.7185e-02, -2.1030e-02],\n",
              "                        [ 1.0187e-02, -3.0167e-02, -8.0193e-03]],\n",
              "              \n",
              "                       [[ 1.7629e-02,  2.5001e-02, -4.5713e-02],\n",
              "                        [ 2.5250e-02,  3.9404e-02,  3.0033e-02],\n",
              "                        [ 1.6265e-02,  5.3023e-02,  8.1163e-03]],\n",
              "              \n",
              "                       [[ 4.1792e-03,  8.5183e-03,  3.5242e-03],\n",
              "                        [ 2.7552e-02,  4.5622e-02,  3.8444e-03],\n",
              "                        [ 2.1285e-02,  2.0103e-02,  1.6193e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.4135e-02, -3.3222e-02, -5.2931e-03],\n",
              "                        [-8.9475e-03, -3.3265e-02,  4.3306e-03],\n",
              "                        [ 1.7092e-02, -2.8524e-02, -2.5048e-02]],\n",
              "              \n",
              "                       [[-1.4275e-02, -2.7405e-02,  1.1635e-02],\n",
              "                        [-2.2799e-02, -3.2017e-02, -9.8221e-03],\n",
              "                        [-2.0387e-02, -3.1692e-02, -9.8188e-03]],\n",
              "              \n",
              "                       [[-2.0245e-02, -7.1923e-03,  5.0653e-03],\n",
              "                        [ 9.2916e-03, -1.4393e-02, -2.8599e-02],\n",
              "                        [ 1.5616e-03, -1.7386e-02, -7.2756e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.4874e-02, -9.5781e-03,  2.7291e-02],\n",
              "                        [ 1.7682e-03, -1.8440e-02,  7.5908e-03],\n",
              "                        [-6.8078e-03, -1.3905e-03,  3.8714e-03]],\n",
              "              \n",
              "                       [[-2.7931e-02, -3.4299e-02, -1.5767e-02],\n",
              "                        [-1.5908e-02, -4.4211e-02, -1.5330e-02],\n",
              "                        [-1.9257e-02, -2.3017e-02,  7.2739e-04]],\n",
              "              \n",
              "                       [[-3.6868e-02,  1.5530e-02, -7.3656e-04],\n",
              "                        [-3.2403e-03, -2.5565e-02, -6.2375e-04],\n",
              "                        [-7.5825e-03, -1.7276e-02, -1.2110e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-3.0703e-03, -3.1071e-02,  4.9807e-04],\n",
              "                        [ 1.3618e-02,  6.6683e-03, -1.3059e-02],\n",
              "                        [-1.5806e-02,  9.5659e-03, -2.8366e-02]],\n",
              "              \n",
              "                       [[-1.5622e-02,  1.0870e-02, -4.3911e-02],\n",
              "                        [-6.0366e-03,  4.7041e-02, -6.7736e-03],\n",
              "                        [ 2.3879e-02,  1.5275e-02,  2.1209e-02]],\n",
              "              \n",
              "                       [[-3.4367e-02, -2.7406e-03, -4.0337e-02],\n",
              "                        [-2.1270e-02, -2.4037e-02, -2.0263e-02],\n",
              "                        [-7.9488e-03, -1.4826e-03, -6.8217e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.0063e-02, -2.2878e-02, -5.5036e-03],\n",
              "                        [ 3.2441e-02, -1.3513e-02,  2.5942e-03],\n",
              "                        [-2.8327e-02, -3.6309e-02, -1.9826e-02]],\n",
              "              \n",
              "                       [[-9.0379e-03,  1.6569e-02,  5.9027e-04],\n",
              "                        [-7.4690e-04,  3.3369e-02,  2.2972e-02],\n",
              "                        [ 1.0572e-02,  8.1467e-03, -3.4821e-02]],\n",
              "              \n",
              "                       [[ 7.4795e-03,  1.9532e-03,  8.9045e-03],\n",
              "                        [-7.3107e-03, -2.9493e-02,  1.2008e-03],\n",
              "                        [ 8.8704e-03,  1.1838e-02,  7.4983e-04]]]])),\n",
              "             ('features.10.bias',\n",
              "              tensor([ 3.7964e-02,  4.3626e-01,  1.9919e-01,  4.7873e-01,  1.9452e-01,\n",
              "                       4.6094e-02,  8.9556e-02, -9.0875e-02,  1.2068e-01,  1.8953e-01,\n",
              "                       4.5016e-02,  4.8434e-01,  2.4634e-01, -7.8553e-02, -4.1008e-02,\n",
              "                      -4.5605e-01,  6.7455e-01,  4.3669e-02, -3.8591e-01,  2.3154e-01,\n",
              "                       1.2107e-01,  3.2227e-01,  1.1335e+00,  2.1153e-01,  4.7995e-01,\n",
              "                       2.8958e-01,  6.4578e-02,  2.4041e-02,  1.5042e-01,  2.8817e-01,\n",
              "                       6.7181e-02,  2.6940e-01,  1.0063e-02,  3.4250e-01,  1.6370e-01,\n",
              "                       2.0520e-01,  7.5118e-02,  1.1070e-01,  2.6571e-01,  1.2281e-01,\n",
              "                      -4.6485e-02,  5.1063e-01,  1.4461e-01,  4.4510e-01,  3.3639e-01,\n",
              "                       4.0720e-01, -2.1050e-02,  1.1052e+00,  1.2309e-01,  3.1051e-01,\n",
              "                       2.3018e-01,  3.6438e-01,  3.8058e-01,  1.7631e-01,  6.3093e-01,\n",
              "                      -1.3756e-02,  2.6251e-01,  2.0362e-01,  3.4236e-01,  1.9062e-02,\n",
              "                       1.5609e-01,  2.2937e-01, -8.7384e-02,  3.1602e-02,  8.6188e-02,\n",
              "                       2.4748e-01, -7.8154e-03,  2.2365e-01,  2.4816e-01,  7.1836e-02,\n",
              "                      -6.6000e-02, -6.8084e-02, -2.1012e-02,  4.0951e-01,  4.2487e-01,\n",
              "                       3.0534e-01, -1.1383e-01,  2.4841e-02,  1.1446e-01,  7.3555e-03,\n",
              "                      -4.6246e-02,  6.3487e-01, -1.1421e-01,  6.1986e-01,  9.2757e-02,\n",
              "                      -1.4665e-02,  4.6250e-01,  6.3386e-01, -1.7848e-02,  1.3755e-01,\n",
              "                       1.2155e-02,  1.9338e-01,  1.9085e-02,  8.1355e-02,  1.7102e-02,\n",
              "                       4.4921e-02,  1.9130e-01,  2.0720e-01,  1.7913e-03,  4.5546e-01,\n",
              "                       3.8208e-01,  3.2588e-01, -1.0084e-01,  5.0784e-01,  1.2523e-02,\n",
              "                       5.9673e-01,  8.0652e-01,  4.6247e-01,  1.4344e-01,  9.7860e-03,\n",
              "                      -3.8480e-02,  3.1713e-02,  1.3493e-01,  5.5403e-01,  1.3879e-01,\n",
              "                       1.8507e-01,  3.5126e-02, -1.2923e-01,  1.5486e-01,  6.6569e-02,\n",
              "                       2.2698e-01, -8.0724e-02,  5.2492e-01,  1.2118e-01,  2.5738e-02,\n",
              "                       2.4021e+00,  5.4695e-01, -1.7379e-01,  1.0619e-01, -4.2293e-02,\n",
              "                       3.7618e-02,  2.4259e-01, -7.6743e-02,  2.1944e-02,  4.8954e-01,\n",
              "                       4.1475e-01,  4.8341e-02, -3.5894e-02,  1.4445e-01, -2.0989e-01,\n",
              "                       2.6906e-01,  6.1454e-02,  4.7476e-03,  5.8720e-01,  2.1773e-01,\n",
              "                       8.1851e-01,  4.1794e-01,  4.1514e-02,  1.7567e-01,  9.2828e-01,\n",
              "                       1.3975e-01,  3.6809e-01,  6.7954e-01, -4.6613e-02,  6.1853e-01,\n",
              "                       9.9139e-02,  3.7578e-01,  1.2078e-01,  7.3212e-01,  1.4217e-01,\n",
              "                      -1.5891e-02,  4.9133e-01,  6.9460e-02,  1.6718e-01,  6.8328e-01,\n",
              "                       3.6984e-01,  1.1704e-01,  4.6448e-03,  4.4395e-01,  1.6502e-01,\n",
              "                       1.2848e-01,  2.1826e-01,  7.0516e-01,  4.1369e-03,  1.6124e-01,\n",
              "                       1.1174e-01, -8.4453e-04,  2.6191e-01,  7.3103e-02,  2.6202e-01,\n",
              "                       1.5253e-02,  2.0889e-01,  3.3991e-01,  3.9360e-02,  9.8699e-02,\n",
              "                       2.8095e-01,  1.3300e-01, -9.8729e-02,  1.5865e-01, -1.0977e-01,\n",
              "                      -1.2899e-01,  1.5089e-01,  1.3981e-01,  1.9367e-03,  1.0875e-01,\n",
              "                      -1.6497e-01,  5.6130e-02,  1.7397e-01,  4.1817e-01,  1.1607e-01,\n",
              "                       3.5296e-01,  8.5897e-01,  2.8839e-01,  1.1828e-01, -5.0673e-02,\n",
              "                       2.8798e-02,  1.3510e-01,  1.9383e-01, -1.9312e-02,  5.4221e-01,\n",
              "                       5.3677e-01,  4.4037e-01,  5.0655e-01,  3.5812e-01,  5.3000e-01,\n",
              "                       5.1179e-01,  2.0096e-01,  3.6999e-01,  6.0126e-01,  4.2610e-03,\n",
              "                       1.9014e-02, -1.7977e-01,  6.5511e-02,  6.4663e-01,  2.4109e-01,\n",
              "                       1.3175e-01,  6.0788e-01, -4.4901e-03,  6.0372e-01,  2.9686e-01,\n",
              "                       6.3624e-01,  3.3710e-01,  1.0049e-01,  4.1112e-01,  5.2909e-01,\n",
              "                       1.8214e-01,  2.3342e-01,  3.5865e-02,  9.3808e-02,  1.6784e-01,\n",
              "                       9.7704e-02,  4.3545e-01,  3.4650e-02, -7.2180e-02,  1.4837e-01,\n",
              "                      -9.3362e-02,  4.1976e-01, -4.9194e-03,  1.8956e-01,  5.4447e-01,\n",
              "                       5.3322e-01,  3.6115e-01,  3.2763e-01,  5.5909e-01,  2.0211e-03,\n",
              "                      -6.6487e-02])),\n",
              "             ('classifier.fc1.weight',\n",
              "              tensor([[-2.0409e-03,  5.6456e-04, -7.0471e-03,  ..., -1.1603e-02,\n",
              "                        7.9697e-03, -1.4133e-03],\n",
              "                      [ 3.3041e-04, -1.0929e-05,  3.7432e-03,  ..., -9.4861e-03,\n",
              "                       -2.8961e-03,  1.3894e-03],\n",
              "                      [-1.5833e-02, -1.2969e-02,  2.2231e-03,  ...,  1.2572e-02,\n",
              "                       -8.3376e-03,  1.3320e-03],\n",
              "                      ...,\n",
              "                      [-4.4836e-03, -1.2062e-02, -1.1486e-02,  ..., -7.7969e-03,\n",
              "                       -1.2710e-02, -1.6816e-02],\n",
              "                      [-3.1767e-03, -1.7347e-02, -1.1268e-02,  ..., -1.6685e-02,\n",
              "                        2.6572e-03, -1.0970e-02],\n",
              "                      [-3.3455e-03, -8.7743e-04,  1.0144e-03,  ...,  3.5048e-03,\n",
              "                       -1.1221e-02, -1.5927e-02]])),\n",
              "             ('classifier.fc1.bias',\n",
              "              tensor([-0.0104,  0.0038, -0.0117,  ..., -0.0130, -0.0021, -0.0134])),\n",
              "             ('classifier.fc2.weight',\n",
              "              tensor([[ 0.0014,  0.0212, -0.0002,  ...,  0.0056,  0.0028,  0.0074],\n",
              "                      [ 0.0039,  0.0085,  0.0205,  ..., -0.0072, -0.0021,  0.0041],\n",
              "                      [-0.0064, -0.0181,  0.0039,  ..., -0.0106, -0.0022, -0.0004],\n",
              "                      ...,\n",
              "                      [-0.0052, -0.0070, -0.0082,  ..., -0.0082, -0.0155,  0.0073],\n",
              "                      [ 0.0049,  0.0136,  0.0127,  ...,  0.0110,  0.0053,  0.0202],\n",
              "                      [ 0.0051,  0.0124,  0.0130,  ...,  0.0104, -0.0173,  0.0091]])),\n",
              "             ('classifier.fc2.bias',\n",
              "              tensor([-0.0197, -0.0013, -0.0381,  ..., -0.0458, -0.0232, -0.0246])),\n",
              "             ('classifier.fc3.weight',\n",
              "              tensor([[ 0.0152, -0.0057, -0.0072,  ..., -0.0228,  0.0030, -0.1062],\n",
              "                      [-0.0314, -0.0019, -0.0533,  ..., -0.0426, -0.0331, -0.0272],\n",
              "                      [ 0.0092, -0.0247, -0.1115,  ..., -0.0054, -0.0298, -0.0461],\n",
              "                      ...,\n",
              "                      [ 0.0206, -0.0247,  0.0084,  ..., -0.0134,  0.0302, -0.0609],\n",
              "                      [ 0.0167,  0.0049, -0.0041,  ..., -0.0274, -0.0489, -0.0364],\n",
              "                      [-0.0258,  0.0086, -0.0221,  ..., -0.0669, -0.0402, -0.0952]])),\n",
              "             ('classifier.fc3.bias',\n",
              "              tensor([ 0.0354, -0.0071,  0.0047,  0.0255, -0.0044, -0.0028, -0.0029, -0.0152,\n",
              "                       0.0195, -0.0172,  0.0013, -0.0088,  0.0500, -0.0061, -0.0239,  0.0268,\n",
              "                      -0.0074,  0.0170,  0.0022,  0.0323,  0.0038,  0.0113, -0.0161, -0.0123,\n",
              "                       0.0025,  0.0457, -0.0378, -0.0012, -0.0114,  0.0461,  0.0082, -0.0406,\n",
              "                       0.0023, -0.0098, -0.0147,  0.0457,  0.0345,  0.0546, -0.0218, -0.0112,\n",
              "                      -0.0139, -0.0540, -0.0007,  0.0002, -0.0375, -0.0202, -0.0198,  0.0104,\n",
              "                       0.0211, -0.0118,  0.0152, -0.0345, -0.0221,  0.0258,  0.0076,  0.0104,\n",
              "                      -0.0082,  0.0178, -0.0293, -0.0097, -0.0147,  0.0272, -0.0247, -0.0171,\n",
              "                      -0.0063, -0.0464,  0.0131, -0.0143, -0.0175, -0.0221,  0.0070, -0.0329,\n",
              "                      -0.0004,  0.0545,  0.0070,  0.0041,  0.0298,  0.0037, -0.0162, -0.0125,\n",
              "                      -0.0242, -0.0162, -0.0256, -0.0041,  0.0536,  0.0210, -0.0202, -0.0206,\n",
              "                      -0.0023, -0.0117, -0.0126, -0.0212,  0.0077, -0.0337,  0.0111,  0.0314,\n",
              "                      -0.0036,  0.0223,  0.0076,  0.0450, -0.0039, -0.0136]))])"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.state_dict ()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghoYftiQYDIr"
      },
      "source": [
        "Save the checkpoint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "KDgETd-cYGRY"
      },
      "outputs": [],
      "source": [
        "model.to ('cpu') #no need to use cuda for saving/loading model.\n",
        "# TODO: Save the checkpoint\n",
        "model.class_to_idx = train_image_datasets.class_to_idx \n",
        "\n",
        "#creating dictionary\n",
        "checkpoint = {'classifier': model.classifier,\n",
        "              'state_dict': model.state_dict (),\n",
        "              'mapping':    model.class_to_idx\n",
        "             }\n",
        "\n",
        "torch.save (checkpoint, 'project_checkpoint.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "-P7np2IBYN6L"
      },
      "outputs": [],
      "source": [
        "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
        "\n",
        "def loading_model (file_path):\n",
        "    checkpoint = torch.load (file_path) \n",
        "    model = models.alexnet (pretrained = True) \n",
        "\n",
        "    model.classifier = checkpoint ['classifier']\n",
        "    model.load_state_dict (checkpoint ['state_dict'])\n",
        "    model.class_to_idx = checkpoint ['mapping']\n",
        "\n",
        "    for param in model.parameters():\n",
        "        param.requires_grad = False \n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVA4-XwvYUA9",
        "outputId": "474d3c99-6495-451b-d315-de8efa774a08"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "AlexNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
              "  (classifier): Sequential(\n",
              "    (fc1): Linear(in_features=9216, out_features=4096, bias=True)\n",
              "    (relu1): ReLU()\n",
              "    (dropout1): Dropout(p=0.3, inplace=False)\n",
              "    (fc2): Linear(in_features=4096, out_features=2048, bias=True)\n",
              "    (relu2): ReLU()\n",
              "    (dropout2): Dropout(p=0.3, inplace=False)\n",
              "    (fc3): Linear(in_features=2048, out_features=102, bias=True)\n",
              "    (output): LogSoftmax(dim=1)\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_verify = loading_model ('project_checkpoint.pth')\n",
        "model_verify"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "9g3d6mZ6YZb1"
      },
      "outputs": [],
      "source": [
        "def process_image(image):\n",
        "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
        "        returns an Numpy array\n",
        "    '''\n",
        "    #size = 256, 256\n",
        "    im = Image.open (image) \n",
        "    width, height = im.size \n",
        "    \n",
        "\n",
        "    if width > height:\n",
        "        height = 256\n",
        "        im.thumbnail ((50000, height), Image.ANTIALIAS)\n",
        "    else:\n",
        "        width = 256\n",
        "        im.thumbnail ((width,50000), Image.ANTIALIAS)\n",
        "\n",
        "\n",
        "    width, height = im.size\n",
        "    reduce = 224\n",
        "    left = (width - reduce)/2\n",
        "    top = (height - reduce)/2\n",
        "    right = left + 224\n",
        "    bottom = top + 224\n",
        "    im = im.crop ((left, top, right, bottom))\n",
        "\n",
        "    \n",
        "    np_image = np.array (im)/255 \n",
        "    np_image -= np.array ([0.485, 0.456, 0.406])\n",
        "    np_image /= np.array ([0.229, 0.224, 0.225])\n",
        "\n",
        "    np_image= np_image.transpose ((2,0,1))\n",
        "\n",
        "    return np_image\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "dgNz8Ly6Ygt3",
        "outputId": "0b808fad-109b-4910-c4c2-18bf305b152a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-29-b4af56b37dbb>:12: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
            "  im.thumbnail ((50000, height), Image.ANTIALIAS)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 417,
              "width": 425
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def imshow(image, ax=None, title=None):\n",
        "    if ax is None:\n",
        "        fig, ax = plt.subplots()\n",
        "\n",
        "    \n",
        "    image = np.array (image)\n",
        "    image = image.transpose((1, 2, 0))\n",
        "\n",
        " \n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    image = std * image + mean\n",
        "  \n",
        "\n",
        "    # Image needs to be clipped \n",
        "    image = np.clip(image, 0, 1)\n",
        "\n",
        "    ax.imshow(image)\n",
        "\n",
        "    return ax\n",
        "\n",
        "image_path = '/content/udacity-image-classification/flowers/train/10/image_07091.jpg'\n",
        "img = process_image(image_path)\n",
        "#img.shape\n",
        "imshow(img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJoBSxB5ZByj"
      },
      "source": [
        "Class Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "xTBFEmGsY6fc"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "def predict(image_path, model, topkl):\n",
        "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
        "    '''\n",
        "    \n",
        "    image = process_image (image_path) \n",
        "\n",
        "    \n",
        "    im = torch.from_numpy (image).type (torch.FloatTensor)\n",
        "\n",
        "    im = im.unsqueeze (dim = 0) \n",
        "    with torch.no_grad ():\n",
        "        output = model.forward (im)\n",
        "    output_prob = torch.exp (output) #converting probability\n",
        "\n",
        "    probs, indeces = output_prob.topk (topkl)\n",
        "    probs = probs.numpy () #converting numpy array\n",
        "    indeces = indeces.numpy ()\n",
        "\n",
        "    probs = probs.tolist () [0] #converting list\n",
        "    indeces = indeces.tolist () [0]\n",
        "\n",
        "\n",
        "    mapping = {val: key for key, val in\n",
        "                model.class_to_idx.items()\n",
        "                }\n",
        "\n",
        "    classes = [mapping [item] for item in indeces]\n",
        "\n",
        "    classes = np.array (classes) \n",
        "\n",
        "    return probs, classes\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "id": "cZhNwIq6ZT8j",
        "outputId": "28c8e431-2b32-4e22-bdb3-bfccbcd8fd6e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-29-b4af56b37dbb>:12: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
            "  im.thumbnail ((50000, height), Image.ANTIALIAS)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 417,
              "width": 425
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 393,
              "width": 590
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "model = model_verify \n",
        "file_path = '//content/udacity-image-classification/flowers/test/1/image_06754.jpg' #an example from test set\n",
        "\n",
        "img = process_image (file_path)\n",
        "imshow (img)\n",
        "plt.show()\n",
        "probs, classes = predict (file_path, model, 5)\n",
        "\n",
        "\n",
        "\n",
        "class_names = [cat_to_name [item] for item in classes]\n",
        "\n",
        "\n",
        "plt.figure(figsize = (6,10))\n",
        "plt.subplot(2,1,2)\n",
        "\n",
        "\n",
        "sns.barplot(x=probs, y=class_names, color= 'green');\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "bW4hCB4sn8Ck",
        "outputId": "d282d1bc-cb56-4c98-d923-995199ab70ed"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_cda5ec51-8f08-48dd-a7b1-1a5bae4821db\", \"project_checkpoint.pth\", 195297781)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "# Download the file\n",
        "files.download('/content/project_checkpoint.pth')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
